The SIMPLEX Procedure
It is a method used for solving the linear program. Linear program generally comprises of the problem related to optimization. It is used for the linear program in its standard form. The standard form of the linear program is:
Minimize:
Subject to condition:
And
Where; the variables of the problem and values are the objective function coefficients.
Now here in the given question, it is to be discussed that the bounding condition for simplex when its loop is replaced by the initialize simplex.
SIMPLEX procedure:
Before getting into the issue, a brief understanding of SIMPLEX procedure is required. Refer to the section 29.3 of the text book for the procedure,
The simplex procedure is useful in making the linear programs. The procedure works on the linear programs that are represented in their standard forms. The procedure takes the standard form of the linear program as the input and yields an n vector and that is one of the optimal remedies to the concerned linear program.
This procedure is specifically helpful when more than one decision variables are available. The procedure works as follows:
The procedure first of all invokes the INITIALIZE-SIMPLEX (A, b, c) procedure in line 1. This finds out if the linear program is viable. If not, the linear program is stored in its slack form. In the third line a loop is initialized.
If all of the coefficients in the concerned objective function of the linear program are negative integers, the loop would terminate. If not, the fourth line of the procedure selects a variable that is that has a positive coefficient.
This will be made the entering variable for the procedure. This variable is chosen using some predefined deterministic rule. Now lines 5 to 9 check all of the constraints. This is meant for getting the one that most vitally limits the amount by which can be extended with leaving the non-negativity constraint intact.
The variable associated with the non-negativity constraint is. Now the leaving variable has to be chosen. Any random variable that follows the predefined deterministic rule can be chosen to play the leaving variable. If the quantity of increment in the entering variable is not controlled by any of the available constraints the line 11 returns “unbounded”.
If this is not true for the scenario, the line 12 of the procedure swaps the entering variable and the leaving variable. This is achieved by invoking the PIVOT procedure. Now the desired solution of the given linear program is calculated using the line 13 to line 16 of the procedure.
Line 13 checks if the encountered variable is the basic or non-basic. If this is basic, the appropriate value is accumulated in the variable in line 15 and if this is not the basic variable then set it to 0 in line 16. The last line of the procedure returns the desired solution for the inputted linear program and that is.
INITIALIZE-SIMPLEX procedure:
The procedure is used to find the basic feasible solution for the problem. This procedure finds out the basic solution if the program is feasible and returns that. If the program is not feasible then the procedure creates a secondary solution and keeps pivoting the secondary form till the time a feasible solution is found.
Even after doing all this if no such solution for the problem is found the procedure returns “infeasible”.
To understand the effect ofthis change a brief understanding of the procedure INITIALIZE-SIMPLEX is required.
Explanation of the procedure:
The first three lines check for the basic solution of the linear program. If the solution is found feasible, the slack form of the program is returned.
If not, the secondary form of the solution is created. This would not be feasible whatsoever. The reason is that the initial solution of the basic form of the problem is not feasible.
Now to find a viable solution pivoting is performed. This is done in the line 8. After pivoting the secondary form, the solution for the problem is searched for. This results in a viable solution. Now to find a complete feasible solution iterative invoke to PIVOT is required till the optimal solution is found.
Once this is found the slack form of the program is found in line 12 and line 13. This slack form is returned with the help of line 15 of the procedure. Even after this if no feasible solution is found for the program, the procedure returns “infeasible”.
So, altogether the procedure either finds a feasible solution for the problem or it just returns infeasible.
Effect of replacing the main loop with INITIALIZE-SIMPLEX:
Now, it is to be checked what would happen if the main loop that is “while” loop of the procedure, is run by a call to INITIALIZE-SIMPLEX.
Suppose that a feasible solution for the linear program exists. Now the initial call to INITIALIZE-SIMPLEX would return the slack form of the linear program. The “While” loop in the SIMPLEX procedure tries to find if this solution of the problem can be bounded by some set of entering or leaving variables or not.
Case 1: The procedure finds the viable solution for the linear program and returns the slack form of the solution.
Case 2: The procedure does not find any viable solution for the linear program and returns “infeasible”.
So whenever the procedure would be called, there would be at least some viable solution found for the program or there would be no feasible solution found.
Case 1:
Now, consider the first case where a feasible solution is found for the program.
If this happens, then being feasible, the solution could always be restricted by some number of entering or leaving variables. In that case the line 11 of the procedure would never be executed.
Case 2:
Now consider the other case:
If no feasible solution for the program could be found, there would be no slack form for the program because there is no solution. If that happens, it makes no sense to talk about the bounded or unbounded form of the solution. Now keeping this in mind, line 11 of the procedure would not be executed, either.
So, even in this the procedure would not return “unbounded”.
Thus, it can be recapitulated from the above discussion that if the “while” loop is run by INITIALIZE-SIMPLEX, there “unbounded” would never be returned regardless of the presence of any feasible solution to the problem.
Linear programming problem (LPP): It is a form of a problem in which maximizing or minimizing an objective is achieved with in the available limited resources and constraints.
The following is the general mathematical representation of the linear programming problem:
Maximize:
Subject to condition:
for
Equation (1) is the objective function and equation (2) and (3) are the constraints for the linear program. Solve the problem for the value of n variables, such that objective function is maximized.
Note: The LPP described in the equations (1), (2), and (3) is also called as the primal of the LLP.
Dual of the Linear Program:
When the LLP is defined for maximization objective, if it is solved for the minimization objective, then the form of LPP used here is known as dual of the given LPP. The original form of the linear program is referred as the primal form.
Minimize:
Subject to condition:
for
for
The dual of the LLP (given by the equations (1), (2), and (3)) is formed by performing the following operations:
• Change the maximization to minimization (vice versa).
• Replace each less-than-or-equal-to with greater-than-or-equal-to in constraints.
• Exchange the roles of coefficients on the right-hand-sides and the objective function.
Optimal Objective value:
For the set of values in the variable, if all the constraints are satisfied, then that set of values in the variable is called the feasible solution. Otherwise the set is infeasible.
is the objective function value of the linear program for which the solution is . The optimal feasible solution is the set of values in the variable among feasible solutions, which yields the maximum objective value.
Optimal objective value of linear program L is 0:
Now prove that optimal objective value of L is 0, if basic solution of both L and dual of L associated with the initial slack forms are feasible.
Proof consists of two steps:
• Extract the meaning of the statement; the fundamental solutions are mixed with the slack form of the feasible solutions in terms of the values of the objective function.
• Get the complete proof with the help of the conclusion drawn.
Step 1:
Basic solution of both L and dual of L associated with the initial slack forms are feasible, so the objective value of L and dual of L can be written as follows:
Where, and are the objective functions for the primal and dual linear programs respectively.
Hence, the fundamental solutions for the primal and dual program of the linear program have zero values for each of the variables in them.
Complete the proof using the weak duality theorem.
Weak duality theorem:
Consider that there is a linear program of the following form:
Maximize
Subject to
and, it’s dual is of the following form:
Minimize
Subject to:
Suppose both of them have feasible solutions. Now, consider to be a feasible solution for primal and to be a feasible solution for dual form.
The, the following inequality holds:
In simple words, the objective function of the primal form of the linear program is bounded by an upper limit and that is the objective function of the linear program’s dual form.
Now, the proof would be completed with corollary 29.9 (Refer to the section 29.4 of the textbook for this).
Consider that the above rule holds for the set of primal and dual form of a linear program that are L and L’ respectively. Along with this, the objective functions for both of the primal and the dual form of the linear program equivalent then and are the optimal remedy to the primal and dual form of the linear program respectively.
As the initial statement states that the fundamental solutions for both L and L’ are feasible, setting the variables values to zero yields:
That means, the value of the objective function of the dual from of the linear program holds a value 0.
Now, as the strong duality rule says that if both of the primal and the dual form of the linear program have the feasible and optimal solutions, then the value of objective function of both of them will be equivalent.
So, if the value of the objective function for the dual form is 0, then it will be 0 for the objective function of primal form.
That is,
Therefore, optimal objective value of L is 0, if basic solution of both L and dual of L associated with the initial slack forms are feasible.
Fundamental Theorem of Linear Programming
Linear programming is a way of achieving the maximum possible outcome from a set of resources that are represented as an integrated mathematical design.
In the technical terms the linear programming is a way to achieve optimum results from the linear programming problems. This optimization is subject to a few constraints.
Standard form of linear program:
In a standard form, there is a set of p real numbers that are , another set of q real numbers that are and a set ofreal numbers that arewhere,
Now, the objective is to find p real numberssuch that
Maximize
… … (1)
And that is subject to
… … (2)
… … (3)
The first expression is called the objective function. The second and the third expression show the constraints.
Fundamental theorem of Linear Programming:
The fundamental theorem of linear programming states the following:
Consider a matrix of rank q. Now, the linear programming says that:
1. If there is a feasible solution for the problem, there does hold a basic feasible solution for the problem.
2. If there is an optimal solution for the concerned problem, there must be a basic optimal solution for the problem.
This theorem can be proved by using the SIMPLEX procedure. The SIMPLEX procedure works subtly regardless the linear program having any feasible solution, infeasible or has an unbounded solution.
By its name, it suggests that it is related to the theory which is the basis for the existence of linear programming. For understanding this theorem, consider any linear program L which is in its standard form so it holds the following criteria:
a. L is unbounded.
b. L is infeasible.
c. L holds an optimal solution which has a finite objective value.
The theorem can be proved by giving separate proves for each of the above mentioned cases.
Proof of Fundamental Theorem of Linear Programming:
Below is illustrated how the SIMPLEX procedure would work for each of the above mentioned cases.
L is unbounded:
Consider the case that there does exist a feasible solution for the procedure but that cannot be bounded.
In this case, procedure SIMPLEX is able to find a feasible solution. Even in that case it might be possible that the solution cannot be bounded by the means of any entering and leaving variable.
If this happens the solution would remain unbounded. In that case the procedure SIMPLEX returns “unbounded” and terminate the program. There would be no slack form of the program achieved in this case.
L is infeasible:
Consider the case that there is no feasible solution for the provided problem. If this happens then the linear program is not feasible. The Procedure tries to find any feasible solution by applying pivoting but the solution would not be found. Now SIMPLEX procedure would not be able to find any feasible solution. Then the SIMPLEX would simple return “infeasible” and terminate the program. This is not possible to find any slack form in that case also.Strict inequalities:
A deep observance of the linear programming infers that the theory holds for only the non-strict inequalities. That means there exist, rules of the form:
Or
That means the right hand side of the expression might either be equal to, greater than or less than the left hand side.
Strict inequality means that the right hand side of the equation would either be greater than or smaller than the left hand side of the expression.
Term strict inequalities means that it will have the form as,
Or
This states that the value of d is strictly greater than or less than the value of term. In a single term, it can be stated as:
That is, the difference of the terms can never be equal to 0.Effect of strict inequality on the linear program:
In no scenario, strict inequality can be allowed for the linear programming. The reason is that if this is allowed for the linear programming, it is not possible to determine any limit for the value of term. That is, the value of term cannot be bounded between two limits. This makes the linear programming lose its basic objective which is to maximize the utilization of resources to any extreme limit.
Consider the following example,
Suppose there is a concrete instance of the linear programming as below:
Maximize
Subject to condition
The program suggests maximizing the value of y to a limit that is greater than 5.
In this case, it is not known what limit the value y is to be maximized to. That means there might any number of values that the value of y could be maximized to. It creates a confusion that makes the linear programming not hold any longer.
Now, it can be concluded that there will not be any value of y for which the maximum will be obtained and so there will not be any feasible solution for this consideration.
So, the above discussion explains that the linear programming cannot be done with strict inequality allowed.
Given linear program is
maximize
subject to
The given linear program is in the standard form. When converting to slack form, use to denote the slack variable associated with ith inequality.
The ith constraint is given by
along with the nonnegativity constraint
The slack form of the given linear program (1) -(5) is
The basic solution of the linear program is
This solution violates the constraint (3) and it is not a feasible solution.
To determine whether a linear program has any feasible solution we, use auxiliary linear program.
Using this auxiliary linear program, a slack is obtained for which the basic solution is feasible. The solution of the auxiliary linear program determines whether the initial linear program is feasible and if so, it provides a feasible solution.
Lemma:
Let be the linear program in the standard form given by
maximize
subject to
Let be a new variable, and let is the auxiliary linear program with variables given by
maximize
subject to
Then is feasible if and only if the optimal objective value of is 0.
The auxiliary linear program equivalent to the linear program given in (1) -(5) is
maximize
subject to
The slack form auxiliary linear program given in (6) -(10) is
The system of equations (11) -(14) has 3 equations and 5 variables. Any settings of the variables defines values for ; therefore, there is an infinite number of solutions to this system of equations. A solution is feasible if all of variables are nonnegative, and there can be an infinite number of feasible solutions. Find a basic solution to a system by setting all the non-basic variables to zero.
The basic solution is
The basic solution has which is not feasible for this auxiliary linear program.
To perform the pivot operation, select as the entering variable. Here is selected as leaving variable, because it is the basic variable whose value in the basic solution is most negative. Switch the role of and.
Solve the equation (13) to get the value of.
Solve the equations (12) and (14) to get on right hand side, substitute using (15) into equation (12) gives
equation (14) gives
Objective function is given by
Using the equations (15) -(18) we get the auxiliary linear program as
The basic solution to the system (19) -(22) is found by setting all the non-basic variables to zero. The basic solution is which is feasible. Precede further to find the optimal solution.
Now reformulate the linear program (19) -(22) so that the basic solution has greater objective value. Select a non-basic variable whose coefficient in the objective function is positive and we increase the value as much as possible without violating any of the constraints. This variable becomes basic and some other variable becomes non-basic. Here choose as the entering variable, because should not be in basic variables. By degenerate pivot operation we make it as non-basic.
Let the variable is selected as leaving variable.
Therefore, switch the role of and. Solve the equation (20) to get.
Solve equations (21) and (22) to get on right hand side, substitute using (23). equation (21) gives
equation (22) gives
Objective function is given by
Using the equations (23) -(26) we get the auxiliary linear program as
All the coefficients in the objective function are negative. Thus, the basic solution is obtained and this is the optimal solution.
The basic solution to the system (27) is found by setting all the non-basic variables to zero. The basic solution is which is feasible. The objective value is 0. The slack form obtained is the final solution to the auxiliary problem.
Note: From the Lemmais feasible if and only if the optimal objective value of is 0.
The solution obtained has, the initial problem (1) -(5) is feasible and just remove it from the set of constraints. The objective function is formulated with appropriate substitutions made to include only non-basic variables.
Setting in (23) -(25) and simplifying, we get the objective function
The slack form is obtained as
This slack form has feasible basic solution and can be solved by simplex method.
The system (28) -(31) has 3 equations and 5 variables. Any settings of the variables define values for; therefore, there is an infinite number of solutions to this system of equations. A solution is feasible if all of variables are nonnegative, and there can be an infinite number of feasible solutions. Find a basic solution to a system by setting all the non-basic variables to zero.
The basic solution is
The objective value is
Now reformulate the linear program so that the basic solution has greater objective value. Select a non-basic variable whose coefficient in the objective function is positive and we increase the value as much as possible without violating any of the constraints. This variable becomes basic and some other variable becomes non-basic.
Now, increase up to as the basic variable also increases. If increased above 5 then becomes negative.
Now, increase up to as the basic variable also increases.
The constraint (30) is the tightest constraint as it limits how much can increased.
Therefore, switch the role of and. Solve the equation (29) to get.
Solve equations (29) and (31) to get on right hand side, substitute using (32).
equation (29) gives
equation (29) gives
Objective function is given by
Using the equations (32) -(35) we get the linear program as
All the coefficients in the objective function are negative. Thus, the basic solution is obtained and this is the optimal solution.
The basic solution to the system (36) is found by setting all the non-basic variables to zero. The basic solution is which is feasible.
The objective value is
Therefore, this solution is the basic solution to the linear program defined by (28) -(31).
The objective value is
Hence, this solution is also the basic solution to the linear program defined by (1) -(5) which is the given linear program.
The objective value is
Therefore, the solution for the given linear program is given by
Optimal basic solution is and objective value is .
The provided linear program is
Maximize
subject to
The given linear program is in the standard form. When converting to slack form we use to denote the slack variable associated with ith inequality. The ith constraint is given by along with the nonnegativity constraint
The slack form of the given linear program (1)-(5) is
The basic solution of the linear program is
This solution violates the constraint (2) and (3) and also it is not a feasible solution.
In order to determine whether a linear program has any feasible solution we will use auxiliary linear program.
Using this auxiliary linear program, a slack form is obtained for which the basic solution is feasible. The solution of the auxiliary linear program determines whether the initial linear program is feasible and if so, it provides a feasible solution.
Lemma:
Let be the linear program in the standard form given by
maximize
subject to
Let be a new variable, and let is the auxiliary linear program with variables given by
maximize
subject to
Then is feasible if and only if the optimal objective value of is 0.
The auxiliary linear program equivalent to the linear program given in (1)-(5) is
maximize
subject to
The slack form auxiliary linear program given in (6)-(10) is
• The system of equations (11)-(14) has 3 equations and 5 variables. Any settings of the variables define values for; therefore we have an infinite number of solutions to this system of equations.
• A solution is feasible if all of variables are nonnegative, and there can be an infinite number of feasible solutions. We find a basic solution to a system by setting all the non-basic variables to zero. The basic solution is
The basic solution has and which is not feasible for this auxiliary linear program.
• To perform the pivot operation, select as the entering variable. Here is selected as leaving variable, because it is the basic variable whose value in the basic solution is most negative. Switch the role of and.
• Solve the equation (13) to get the value of.
• Solve equations (12) and (14) to get on right hand side, substitute using (15) Equation (12) gives
Equation (14) gives
Objective function is given by
Using the equations (15)-(18) we get the auxiliary linear program as
The basic solution to the system (19)-(22) is found by setting all the non-basic variables to zero. The basic solution is which is feasible. We precede further to find the optimal solution.
• Now reformulate the linear program (19)-(22) so that the basic solution has greater objective value. Select a non-basic variable whose coefficient in the objective function is positive and we increase the value as much as possible without violating any of the constraints.
• This variable becomes basic and some other variable becomes non basic. Here we choose as the entering variable, because should not be in basic variables. By degenerate pivot operation we make it as non-basic.
Let the variable is selected as leaving variable.
Therefore switch the role of and. Solve the equation (20) to get.
Solve equations (21) and (22) to get on right hand side, substitute using (23). Equation (21) gives
Equation (22) gives
Objective function is given by
Using the equations (23)-(26) we get the auxiliary linear program as
All the coefficients in the objective function are negative. Thus, the basic solution is obtained and this is the optimal solution.
• The basic solution to the system (27) is found by setting all the non-basic variables to zero.
• The basic solution is which is feasible. The objective value is 0. The slack form obtained is the final solution to the auxiliary problem.
Note: From the Lemmais feasible if and only if the optimal objective value of is 0.
The solution obtained has, the initial problem (1)-(5) is feasible and we can just remove it from the set of constraints. The objective function is formulated with appropriate substitutions made to include only non-basic variables.
Setting and simplifying, we get the objective function
Also set in the equations (23)-(25) to obtain the slack form as
This slack form has feasible basic solution and now we can solve it by simplex method.
The system (28)-(31) has 3 equations and 5 variables. Any settings of the variables define values for; therefore we have an infinite number of solutions
The objective value is
Now, reformulate the linear program so that the basic solution has greater objective value. Select a non-basic variable whose coefficient in the objective function is positive and we increase the value as much as possible without violating any of the constraints. This variable becomes basic and some other variable becomes non basic.
The variable is selected as it is has positive coefficient in the objective function.
If increased above 3 then becomes negative.
If increased above 1 then becomes negative.
If increased above then becomes negative.
The constraint (31) is the tightest constraint as it limits how much can increased.
Therefore switch the role of and. Solve the equation (31) to get.
Solve equations (29) and (30) to get on right hand side, substitute using (32).
Equation (29) gives
Equation (30) gives
Objective function is given by
Using the equations (32)-(35) we get the linear program as
We find a basic solution to a system by setting all the non-basic variables to zero. The basic solution is
The objective value is
Now again reformulate the linear program so that the basic solution has greater objective value. Select a non-basic variable whose coefficient in the objective function is positive and we increase the value as much as possible without violating any of the constraints. This variable becomes basic and some other variable becomes non basic.
The variable is selected as it is the only variable with positive coefficient in the objective function.
We can increase up to, the basic variables also increases to.
Hence the given linear programming problem given by (1)-(5) is unbounded.
Variation of Dual and Primal
Linear programming is used to find out the best outcome from a given number of equations. In linear programming, a list of requirements is considered.
For better understanding of the question, a shallow look at primal and dual of the linear programming would be required. In the linear program, every primal has its corresponding dual.
It is shown below,
Primal LP (P) :
Find values for the n variables:
Maximize
… … (1)
Subject to condition
for i=1, 2… m. … … (2)
for j=1, 2… n. … … (3)
Here, equation (1) is the objective function and equations (2) and (3) are the constraints for the objective.
Dual LP (D) :
Find values for the m variables:
Minimize
… … (4)
Subject to condition
for j= 1, 2… n. … … (5)
for i=1, 2… m. … … (6)Here in the problem, a 1-variable linear program is given in which there is a primal P. It also has a dual D.
Primal:
Consider the primal P given below:
Maximize
Subject to constraint
Where r, s, t are arbitrary real numbers. And x is the new defined variable for dual.
Dual of P:
The dual D of this variable program is given below:
Minimize
Subject to constraint
Now, there are three kinds of the solution that can be found for the concerned linear programs. They are discussed as below:
Optimal solution: Optimal solution is a solution which provides the best suited solution for the equations. When there is more than 1 solution of linear programming, then the best solution as per requirement is the optimal solution.
Feasible solution: In linear programming, a feasible solution is a solution which provides a single or infinite number of solutions. In this case, many of the conditions are satisfied which provides optimal solution.
Infeasible solution: In linear programming, an infeasible solution is a solution which does not have any solution. In this case, the equations provide a parallel line.
There are certain implications of the feasibility of the primal and the dual form of the linear program.
1. If there exists a feasible solution for the primal form of the program and that is and for the dual form that is. Then
2. If there exists a feasible solution for the primal form of the program, that is but not for the dual form of the program. Then
That means
3. If there exists a feasible solution for the dual form of the program, that is but not for the dual form of the program. Then
That means
4. There are no feasible solutions to either of the primal or the dual form of the linear form.
Now, the condition is to be asserted on primal and dual for various values of r, s and t.
1. Both primal and the dual have optimal solutions with the finite objective values. For this to happen both primal and dual have feasible solution. Moreover, keeping the previously discussed implications in mind the value of objective function of the primal form would be equal to the objective function of the dual form of the linear program.
To find the feasible solution for both of the primal and the dual form, any of these values can be used,
a.and.
b. and.
c. and has an arbitrary value.
In case a, the value of r is taken as less than 0, value of s is equal to or greater than 0 and value of t is either equal to or greater than 0. This condition provides the optimal solution and SIMPLEX returns “feasible”.
In case b, the value of r is made equal to 0, value of s can be either greater than or equal to zero and the value of t remains same as that in case a. In this, the SIMPLEX returns “feasible”.
In case c, the value of r is made greater than 0, the value of s remains same as in case b and value of t can take any of the arbitrary values. The value returned by SIMPLEX is “feasible”.
2. Primal P is feasible, but dual D is infeasible.
In this case the value of the objective function of the primal function would tend to negativity.
It is possible in two ways as,
a. and.
b. and.
In both ways, a and b, the value of r is taken either 0 or greater than 0 or less than 0. The value of s is less than or equal to zero and the value of t is taken greater than 0. These cases make the program P as feasible and it is dual D as infeasible.
3. Dual D is feasible but primal P is infeasible.
In this case the value of the objective function of the dual form must tend to infinity.
This condition is possible only when
, and
In the condition given above, the value of r is taken as either equal to or greater than 0 and the value of is taken as less than 0. This condition will provide infeasible solution for P and feasible solution for its dual D.
4. Neither primal P nor the dual D is feasible. It is possible when and.
When the value of r is made equal to zero, the substitution of the value in the respective objective function and the constraints would yield the values of s and t.
Now the value of s is less than 0 and value of t as greater than 0.
This leads to providing infeasible solution for program P and its dual D.


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I reside in Central PA with my husband and 3 children. I attended College at Bloomsburg University in accounting. After having children I wanted a career that was flexible. I have been as a Realtor for over 7 years. I've always had a desire to learn new things. My most recent adventure that I have embarked on is enrolling at Penn LPS Coding Bootcamp. Over the next 24 weeks, I plan to learn and gain the skills necessary to begin a new path. A path that allows me numerous avenues to explore , within the coding world, and let my creativity soar. Ultimately, I hope to possess the skills to work remotely when my husband retires. His dream is to travel the world by sea.
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helloRPC implements a simple channelRPC service.
On linux in the helloRPC directory enter:
scripts/helloService
There is also a scripts/helloService.bat file for windows.
On linux in the helloRPC directory the following are examples of running the client
mrk> scripts/helloClient Hello anonymous mrk> scripts/helloClient Me Hello Me mrk>
There is also a scripts/helloClient.bat file for windows.
This is a starting point for writing an RPC service without using pvDatabaseJava.
But note that example database has a similar example implemented via pvDatabaseJava, which allows clients to monitor the result of each request.


MARS ATTACKS! A film review by Scott Mendelson Copyright 1997 Scott Mendelson
1996 103 MIN. rated "PG-13" (extreme stylized death and destruction, brief gore, slight profanity)
Directed by Tim Burton
Starring:
Jack Nicholson, Glenn Close, Annette Benning, Pierce Brosnan Jim Brown, Sarah Jessica Parker, Lucas Haas, Natalie Portman Tom Jones, Sylvia Sidney, Rod Steiger, Paul Winfield, Martin Short, Michael J. Fox, Danny Devito, Joe Don Baker, Pam Grier, Lisa Marie, Jack Nicholson, The Martians
This article should probably be taken with a grain of salt due to the fact that I believe Tim Burton to be a God among men, incapable of error and my idol in the filmmaking world.
"Ack-Ack-Ack-Ack!" translation: Mars Attacks! is not only the funniest film of 1996(but, for artistic merit, Fargo is tops for 96), but the funniest film that I've seen since Airplane!! It's a terrific mixture of top-notch writing and acting, THE best computer animation I have ever seen, and, best of all, a healthy dose of VERY macabre bad taste. After thousands of martian ships are spotted surrounding Earth, the President(Jack Nicholson), the First Lady(Glenn Close), the scientist(Pierce Brosnan), the press secretary(Martin Short), and the generals(Rod Steiger and Paul Winfield) try to decide what to do. Brosnan and Winfield suggest a peaceful welcome while Steiger suggests they declare war on these bug-eyed, big brained "things". An official meeting is set up in a desert, several of the martians step out to greet the thousands of on-lookers, and they declare that they come in peace. At this point, they whip out their ray guns and take out hundreds of earthlings, including several of the big stars. In fact, one of the main pleasures of this epic is the growing realization that few of your favorite stars are going to make it to the end credits.  While politicians and entrepreneurs are blown to bits, a down on his luck boxer and a southern kid, who rush to save their grandmothers, live. As usual, Burton sides with the outsiders. We love him for it. Be warned, this is a very, very sick movie.   And, again, unlike ID4, where popcorn was thrown at my head for doing so, you can root for the aliens, because Burton is rooting for them too. Technically, the martians look great. With greenish-blue skin tone, bulging eyes, and huge brains, they don't speak English, they do understand when earthlings talk. They speak in some kind of gibberish, and Earth uses a translator to communicate with them: "ACK, ACK, ACK, ACK, ACK!" translation: "We Come in Peace!" The best thing about this film is that, unlike most invasion flicks, where they just feel like props, the aliens actually have a personality. The FX are so convincing, and the invaders are so unique in nature that the martians become full-blown characters.  They actually take time to enjoy the mayhem while performing demented experiments on those poor earthlings(wait till you see what happens to Pierce Brosnan and Sarah Jessica Parker) and destroying many a famous landmark(the Easter Island statues, the Eiffel Tower, etc.) with glee. At times, they seem to be like a distant cousin to the Gremlins, but a lot smarter. While the film's first 40 minutes introduces its many human characters and thus, is occasionally dull(grade: C+) the last hour is pure "Burton-ion" craziness(A+++). And although I wanted the martians to triumph, the way that the martians finally meet their doom is one of the decade's best gags(and certainly more creative than ID4's: "let's give them a computer virus" snore). While obviously not for all tastes, the film has probably got the most creative way ever for disposing of a hydrogen bomb. A+
Note: Despite my many objections, it may be worth noting that I did enjoy Independence Day as fun, slick, big-budget, garbage. But Ronald Emmerich is no Tim Burton.
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Domination
Which house do you want:
Lets do it again. Given Fig4 of this paper what options would you reject?
Congraulations, you've just worked out how to do multi-objective optimization
- Build Popluation[ i=0 ] by making lots of guesses
- Experiment: (mutate, cross over)
- Select the best results (a.k.a know as find the Pareto frontier)
- Set Population[ i+1 ] to the frontier.
- Goto step1
"Give me the fruitful error any time, full of seeds, bursting with its own corrections. You can keep your sterile truth for yourself."
― Vilfredo Pareto
"For many events, roughly 80% of the effects come from 20% of the causes."
― Vilfredo Pareto
Domination measures:
Binary Domination
- X domiantes Y if better on at least one, and worse on none.
- Returns a boolean.
- Widely used. Often stops distinguishing things after 3+ goals.
- Insenstive to the size of the domnation
Indicator Domiantion
From Zitler 2004:
- X dominates Y if moving X to Y loses less than moving Y to X.
- To make the signal stronger, we shout the difference (raise it to some expendial power).
- To gauge the overall signal, we average across all goals
- To handle goal we want to minimize, maximize, we add a "w" constant (-1,1).
- Returns a number.
-
Better at distinguishing higher problems with 3+ goals, see Table8.
-
To rank rowX, I
- N=100 times (say), pick rowY at random.
- Q: Why not comapre to all rows?
- If indicatorDomiantes( X,Y ) then +1/N to rowX
- Then I add a new
dom
column to the data, wheich we want to maximize. - see dom.html
- N=100 times (say), pick rowY at random.
For example, here are some rows of auto.csv:
%cylinders displacement horsepower <weight >acceltn model origin >mpg
8 340:360 150:160 3609 8 70:70 1 10
6 198:225 83:86 2587 16 70:70 1 20
8 383:455 165:230 4425 10 70:70 1 10
8 302:305 120:140 3449 10.5 70:70 1 20
6 198:225 92:97 2833 15.5 70:70 1 20
8 383:455 165:230 3850 8.5 70:70 1 20
4 104:114 92:97 2372 15 70:70 3 20
4 104:114 92:97 2375 17.5 70:70 2 30
8 340:360 165:230 3693 11.5 70:70 1 20
....
From the first row, we see we want to minimize weight and maximize acceleration and mpg. From the other rows, we see that some discretizer has gotten to the displacement and horsepower values are replaced them with some string describing a range e.g.lo:hi = 340:360.
Here's the same data, with dom
score added. Shown here are the 5 best and worst rows.
%cylinders displacement horsepower <weight >acceltn model origin >mpg >dom
8 383:455 165:230 4746 12 71:71 1 10 0
8 383:455 165:230 4951 11 72:73 1 10 0
8 383:455 165:230 4952 11.5 72:73 1 10 0
8 383:455 165:230 4955 11.5 71:71 1 10 0
8 383:455 165:230 5140 12 71:71 1 10 0
8 383:455 165:230 4354 9 70:70 1 10 0.01
8 383:455 165:230 4425 10 70:70 1 10 0.01
8 383:455 165:230 4464 11.5 71:71 1 10 0.01
8 383:455 165:230 4735 11 72:73 1 10 0.01
8 383:455 165:230 4906 12.5 72:73 1 10 0.01
.. ... ... ... .. ... ... ... ...
4 85:91 69:72 2070 18.6 78:78 3 40 0.98
4 85:91 ? 1835 17.3 80:80 2 40 0.98
4 68:85 46:65 1825 18.6 77:77 2 40 0.99
4 68:85 69:72 1613 18 71:71 3 40 0.99
4 85:91 46:65 2335 23.7 80:80 2 40 0.99
4 85:91 46:65 1968 18.8 80:80 3 40 1.0
4 85:91 46:65 1975 19.4 81:81 3 40 1.0
4 85:91 46:65 1985 21.5 78:78 2 40 1.0
4 85:91 46:65 2085 21.7 80:80 2 40 1.0
4 96:97 46:65 2130 24.6 82:82 2 40 1.0
Observe that the highest dom scores are assocaited wiht rows with least weight, most acceleration and most mpg (and the lowest dom scores are associated with the reverse).
The Lay of the Land
The real story is that underneath surface features of all these problems is a common mathematical structure called, you guessed it, the landscape.
To know the landscape is to know how to optimize, how to avoid getting stuck on being over-adapted (hence overspecialized) on some local peak, when as Stewart Brand so aptly puts it...
- "Whole mountain ranges of opportunity could be glimpsed in the distance, but getting to them involved venturing 'downhill' into regions of lower fitness".
Studying such landscapes made Brand distrust claims for "optimality" since what you call "optimum" may actually be just a stepping zone to a better place.
Brand's favorite landscape comes from a 1932 genetics paper that discusses how different breeding strategies respond well (or badly) to environmental pressures. In the following, the x-y axis might be "length of hair" and "body weight" and the z-axis might "probability of winning a fight".
Says Brand:
-
"The first two illustrate how low selection pressure or a high rate of mutation (which comes with small populations) can broaden the range of a species whereas intense selection pressure or a low mutation rate can severely limit a species to the very peak of local fitness. The third diagram shows what happens when the landscape itself shifts, and the population has to evolve to shift with it."
-
"The bottom row explores how small populations respond to inbreeding by wandering ineffectively. The best mode of exploration Wright deemed the final diagram, showing how a species can divide into an array of races that interact with one another. That jostling crowd explores well, and it can respond to opportunity."
Holes, poles, saddles, local minima, flat, brittle
So to understand search, understand the landscape. If you know the landscape, you can see where it can trap and where it can help us out. One such trap is the saddle which, in the above diagram is the flat space between the mountain (called a pole) and the hole next to it.
Note that if walk along the saddle, you might think you are in a stable space of solutions. But be warned, one slip to the left or right and the landscape changes dramatically. In the above space you might fall into a hole or a pole.
Another trap is the local minima that seems like a good idea but if you get sucked into it, you may never find the much better place:
Another bad landscape is one that is completely flat. Try as you like, you walk along way around this one before finding anything better or worse:
The opposite of flat is bumpy:
Bumpy landscapes are common so Harman comments that understanding the neighborhood of our solutions is an open and pressing issue in search-based software engineering (SBSE):
- "In some software engineering applications, solution robustness may be as im-portant as solution functionality. For example, it may be better to locate an area of the search space that is rich in fit solutions, rather than identifying an even better solution that is surrounded by a set of far less fit solutions."
- "Hitherto, research on SBSE has tended to focus on the production of the fittest possible results. However, many application areas require solutions in a search space that may be subject to change. This makes robustness a natural second order property to which the research community could and should turn its attention."
Bumpy landscapes mean that, sometimes, to achieve better goals you may have to first give up some of your current achievements. In the history of A.I. this is also called Sussmann's anomaly- that sometimes the way to "better" is via "worse". There are many ways to "jump over" these anomalies. Sussman (and his supervisor, Marvin Minsky) believed that intelligence requires an exolicit list of exceptions or tricks and that any plan for making things better had better be auditted by a "debugging" system. That is a knowledge-full approach that requires some analyst to supply descriptions of "when to jump around". Alternate knowledge-less approaches are:
- Stochastic jumps using, say, simulated annealing;
- The retries mechanism discussed below;
- Momentum constants added to the mutators of stocahstic search. Such momentum constants resist sudden stops to the search: if a local maximum is reached, the momentum constant would push the inference a little further just to see if anything better lies beyond the current position. For example, in the following landscape, momentum would nudge the ball over that little lip into a better (and lower) world.
Which is best: knowledge-full or knowledge-less? Well, that depends on the nature of the problem, the intrinsic value of the knowledge, etc etc. But the general engineering trade-off is that knowledge-less approaches are faster to build and maintain, while the knowledge-full approaches perform comparatively better. FYI- I used to work on knowledge-full approaches but I have found my life to be easier since I switched to knowledge-less.
Walkting the Territory
A.k.a. multi-objective domination


Are the other radar indicators work? On Profile for example.
Thanks Juress for reporting this for me. Didn't mean to get anyone yelled at....sheesh
Anyway yeah, after this latest Chrome update and the latest BBLog update installed, users on my "user radar" list do not show up below the server name anymore. I have confirmed that my user list is populated with users and users in that list are currently on the server.
If I click on their profile the Radar indicator light is lit, however, they do not appear under the server name in the server browser area.
The "server radar" function seems to be working properly.
Not sure what to say, this problem just started after these latest updates. Maybe i should try to re-install the plugin. just though i would mention it in IRC to see if others were having the same problem.
Thanks again juress.....


---
layout: post
title: Backhanded Apple Marketing
date: '2009-05-21T09:42:00.004-05:00'
author: Jabell
tags:
- Fun
- Apple
- Culture
modified_time: '2010-12-10T13:33:32.421-06:00'
blogger_id: tag:blogger.com,1999:blog-19739956.post-3012632940683896075
blogger_orig_url: http://theabellsix.blogspot.com/2009/05/backhanded-apple-marketing.html
---
Windows Secrets, a popular online newsletter for Windows users, had the following lead-in to their top story, written by Woody Leonhard:
If you've ever wondered why it's so difficult to manage and share files in Windows, you'll be delighted with two significant new features in Windows 7.Hah! Why not just use a Mac and avoid all the MS hassle?
These new capabilities, called Libraries and Homegroups, make finding files and connecting with resources on other PCs so easy you'll think you're using a Mac!


XYRINE ACADEMY
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PART 2 - EMPLOYMENT HISTORY
EMPLOYMENT 1: Please start with your present or most recent employment, stating the following: Name and Address of Employer, Position Held, Salary, Dates (e.g. Sep 2001-Aug 2003), Reason for Leaving:
EMPLOYMENT 2: Please also state the following: Name and Address of Employer, Position Held, Salary, Dates (e.g. Sep 2001-Aug 2003), Reason for Leaving:
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Please continue in this box if more space is needed
PART 3 - EDUCATION AND QUALIFICATIONS
INSTITUTION 1: Please start with the most recent institution attended and work backwards to Nursery/Primary school stating the following: Name and Address of the Institution, Dates Attended, Qualifications Obtained (including subjects, grades, class and division)
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PART 4 - EXTRA-CURRICULAR ACTIVITIES/ADDITIONAL SUBJECTS
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Please make full use of this section to provide further information in support of your application. Please be sure to show how your education, training, experience and qualifications fit you for this post. Short listing will be conducted only on the basis of how you have demonstrated how you intend to add value beyond the norm. It is therefore essential that all applicants sell themselves in this section of the application in order to be successful.
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Spreading the word is as easy as telling a friend.
You can even help them get started. Wouldn't that make you such a great friend?


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Bravo pack
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The time is now for it to be okay to be great. For being a bright color. For standing out.
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We get insulted by others, lose trust for those others. We get back stabbed by friends.
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These are the popular apps on Huawei’s AppGallery in South Africa
by Andy WalkerHuawei, at its Developer Day in Cape Town on Thursday, revealed more about its budding ecosystem, growth in South Africa, and its AppGallery.
The AppGallery is effectively Huawei’s version of the Google Play Store, serving as an app respository for users. It’s pretty damn important too.
As we’ve seen by the Huawei Mate 30 earlier this year, which launched without Google Services support, the AppGallery could soon become the only way to download apps to Huawei and Honor smartphones.
And that seems to be a firm focus for the company.
“Operating system is not the difficult part,” said Akhram Mohamed, Huawei CBG CTO for SA.
“On a consumer level, they don’t really engage on the OS level, but rather the app level.”
It noted that 350-billion apps have been downloaded by users from the AppGallery since its inception.
But which apps are the most popular in South Africa?
DStv Now is currently the biggest video app on AppGallery, seeing 72 000 downloads in the past two weeks.
Takealot is the top shopping app in South Africa, with a 20% monthly increase in downloads. It currently sits at 214 000 downloads.
News24’s app has been downloaded 52 000 times, the company added, while another video app called Viu has seen 100 000 downloads in a month.
“We are not forgetting about South Africa. South Africa is an incredibly important market for us,” Mohamed added.
This promise also comes after the company confirmed in May that more apps from local developers will be featured on the AppGallery in the future.
Huawei also shed light on its market share in South Africa at its Developer Day, which continues on Thursday.
Feature image: Andy Walker/Memeburn


Hello {client_name},
Administrator has change your account ({email}) status to {status}.
Best regards, {site_name} {site_link}


Methods
- D
- L
- R
- S
Instance Public methods
logs_tree()
Link
Source: show
# File app/controllers/refs_controller.rb, line 30 def logs_tree contents = @tree.contents @logs = contents.map do |content| file = params[:path] ? File.join(params[:path], content.name) : content.name last_commit =<EMAIL_ADDRESS>file, 1).last last_commit = CommitDecorator.decorate(last_commit) { file_name: content.name, commit: last_commit } end end
switch()
Link
Source: show
# File app/controllers/refs_controller.rb, line 11 def switch respond_to do |format| format.html do new_path = if params[:destination] == "tree" project_tree_path(@project, @ref) else project_commits_path(@project, @ref) end redirect_to new_path end format.js do @ref = params[:ref] define_tree_vars render "tree" end end end
Instance Protected methods
define_tree_vars()
Link
Source: show
# File app/controllers/refs_controller.rb, line 45 def define_tree_vars params[:path] = nil if params[:path].blank? @repo = project.repo @commit = project.commit(@ref) @commit = CommitDecorator.decorate(@commit) @tree =<EMAIL_ADDRESS>project, @ref, params[:path]) @tree = TreeDecorator.new(@tree) @hex_path = Digest::SHA1.hexdigest(params[:path] || "") if params[:path] @logs_path = logs_file_project_ref_path(@project, @ref, params[:path]) else @logs_path = logs_tree_project_ref_path(@project, @ref) end rescue return render_404 end


Apprenticeship programme
Apprenticeships can be added to your progamme one at a time or by importing them from an external file.
This commitment has 0 apprenticeships
This commitment has apprenticeship
| Training type | GivenName | Family Name |
ULN | Start date |
End date |
Cost |
|---|
This commitment has 5 apprenticeships
| Training type | GivenName | Family Name |
ULN | Start date |
End date |
Cost | |
|---|---|---|---|---|---|---|---|
| Mechatronics Engineer | David | Edwards |<PHONE_NUMBER> | Nov 2018 | Oct 2020 | £1,500 | View |
| Mechatronics Engineer | Rob | Andrews |<PHONE_NUMBER> | Nov 2018 | Oct 2020 | £1,500 | View |
| Mechatronics Engineer | Mellisa | Shaw |<PHONE_NUMBER> | Nov 2018 | Oct 2020 | £1,500 | View |
| Mechatronics Engineer | Brendan | Hamazaki |<PHONE_NUMBER>1 | Nov 2018 | Oct 2020 | £1,500 | View |
| Mechatronics Engineer | Gary | Barton |<PHONE_NUMBER>6 | Nov 2018 | Oct 2020 | £1,500 | View |


URLs have been tricky little devils over the years. Mainly because various implementations have diverged in subtle details of parsing them.
The following test pages are designed to test URL parsing in the described ways.
URL Live Viewer
Display a URL's parsed components from both the browser's DOM, and from the URL.js project being prototyped for the WHATWG URL spec.
URL Test Runner for 500+ URL test cases
Run all tests from urls.json using testharness.js to compare the Web browser's DOM properties with the expected result.
Test URL parsing in HTTP GET requests
WARNING: This isn't implemented yet
Run all tests from urls.json using testharness.js to compare the Web browser's DOM properties with the resultant HTTP request's path and hostname parts.


AREAS® Customer
Assistance Agreement
IMPORTANT – PLEASE READ THIS AREAS
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REMOTE ASSISTANCE CREDITS MADE AVAILABLE THROUGH THIS WEB SITE (AREAS.com).

BACKGROUND
CUSTOMER has licensed certain AREAS
software, services or other products from AREAS that are eligible for the
Services, as defined below, (the “Software”) pursuant to an applicable end user
license agreement or terms of service (“Software Terms”).
CUSTOMER wishes to receive
certain professional support services related to Customer’s use of the Software
pursuant to the terms and conditions set forth in this Agreement.
SECTION 1 – SUPPORT SERVICES
1.1 Scope of Services
1.1.1 AREAS shall provide to
CUSTOMER support services directly related to the implementation of the
Software (the “Services”). In the event that CUSTOMER requests additional
services outside the scope of the Services, the parties may agree to provide such
services pursuant to a separate document executed by both parties. The Services shall be purchased by CUSTOMER
in indivisible, nontransferable one (1) hour blocks to be provided remotely
from AREAS’s premises (each single block a “Remote Assistance Credit”). A
minimum of one (1) Remote Assistance Credit is required for any Services to be
performed. Remote Assistance Credits may be applied consecutively, however, it
is Your responsibility to purchase sufficient Remote Assistance Credit’s for
the Services. Remote Assistance Credits purchased for one type of Software
cannot be used for Services related to another type of Software. For example,
Test Studio Remote Assistance Credits can only be redeemed for Services related
to Test Studio.
1.1.2 CUSTOMER shall initially
request the Services by submitting a support ticket describing the requested
Services. CUSTOMER and AREAS will then agree upon the scope of Services to be
provided. At CUSTOMER’s election, AREAS software engineers may remotely access
CUSTOMER’s machine, AREAS account, including any related accounts and anything
stored in, supported by, or associated with such accounts, using desktop or
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1.1.3 It is understood and agreed
that the Services may include advice and recommendations, but all decisions in
connection with the implementation of such advice and recommendations shall be
the responsibility of, and made by, CUSTOMER.
1.2 Exclusions and Limitations on
Services. Services are not available or provided hereunder for or related to:
1.2.1 Machine error;
1.2.2 Customer’s failure to
follow operating instructions;
1.2.3 Customer’s unlicensed use
of the Software and/or use of the Software that is not in accordance with the
Software Terms and/or any applicable documentation;
1.2.4 Customer’s negligence or
improper use of the Software;
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Software (whether or not permitted under the Software Terms) by any person or
entity other than AREAS;
1.2.6 Any third-party or open
source component(s) of the Software;
1.2.7 Any data or software other
than the Software, including, but not limited to, application programs,
databases, files, source codes, object codes or proprietary data, or any
configuration, installation or reinstallation of any software or data. You are
responsible for backing up copies of all your applications, content and any
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1.2.8 Any custom development,
integration or implementation, project upgrades or project migrations; or
1.2.9 Any installation,
deployment, or use of the Software: (i) in any country or by any individual or
entity subject to U.S. Export restrictions; (ii) for any purpose(s) which AREAS
determines in its sole discretion is/are unlawful, immoral, offensive, threatening,
libelous, defamatory, pornographic, obscene or otherwise objectionable, or in
violation of any party’s privacy or intellectual property rights; (iii) in any
situation where failure or fault of the Software could lead to death or serious
bodily injury of any person, or to physical or environmental damage.
1.3 If it is determined by AREAS
in its sole discretion that you have received support from AREAS for which you
were not eligible or to which the above exclusions and limitations apply, AREAS
may assess and you agree to pay reasonable and appropriate fees for such
support.
SECTION 2 - PERSONNEL
2.1 AREAS's Services shall be
rendered by its employees and/or independent contractors. AREAS shall ensure that all employees are
familiar with and comply in all respects with the provisions of Section 3
(Confidentiality) of this Agreement, and that all independent contractors have
agreed to terms no less protective of CUSTOMER’s confidential information than
this Agreement.
2.2 CUSTOMER will not, at any
time during the term of this Agreement and for a period of one (1) year
following the expiration or termination hereof, directly or indirectly, employ,
offer to employ or otherwise engage or solicit for employment or a contractor
relationship any person who is now or at any time during the term of this
Agreement, an employee of AREAS or any of its affiliates, without the other AREAS’s
written consent.
SECTION 3 - CONFIDENTIALITY
3.1 For purposes of this
Agreement, "Confidential Information" means all information
disclosed, directly or indirectly, through any means of communication (whether
electronic, written, graphic, oral, aural or visual), by or on behalf of one
party (the “Discloser”) to or for the benefit of the other party (the “Recipient”)
or any of its personnel, that relates to:
1.Discloser’s products, services,
projects, productions and work product, and all business and technical
information pertaining thereto (including, without limitation, reports,
analyses, budgets, and technology (including prototypes, designs,
specifications and implementations));
2.Discloser’s research and
development, asset management, production pipelines and technologies,
development strategies, techniques, processes and plans, intellectual
properties, trade secrets and technical know-how;
3.Discloser’s administrative,
financial, purchasing, information systems, telecommunications technology,
distribution, marketing, labor and other business operations, policies and
practices; and
4.Any other matter disclosed in
written or other tangible form, that is clearly marked as being confidential or
if disclosed orally, within ten (10) days of such oral disclosure is reduced by
Discloser to a writing and marked as confidential and supplied to the other
party.
3.2 Recipient agrees that during
the term and for a period of two (2) years thereafter it will
1.not use, or authorize the use
of, any Confidential Information for any purpose other than solely to perform
the Services;
2.protect all Confidential
Information with the same degree of care (but no less than a reasonable degree
of care) normally used to protect its own confidential information; and
3.take reasonable steps to
prevent any Confidential Information or any information derived therefrom from
being revealed to any person or entity other than to (i) those of its personnel
(such term to include independent contractors) who have a legitimate need to
know the Confidential Information to perform the Services and who are bound by
confidentiality obligations prohibiting the further use and disclosure thereof,
and (ii) those to whom Discloser has authorized in writing the disclosure of
the Confidential Information. CUSTOMER shall ensure that all employees are
familiar with and comply in all respects with the provisions of this Section
(Confidentiality) of this Agreement, and that all independent contractors have
agreed to terms no less protective of AREAS’s confidential information than
those contained herein.
Recipient further agrees that in
the event that it receives a request from any third party for any Confidential
Information, or is directed to disclose any portion of any Confidential
Information by operation of law or in connection with a judicial or
governmental proceeding or arbitration, Recipient will promptly notify
Discloser prior to such disclosure and will consult with Discloser in seeking a
suitable protective order or assurance of confidential treatment and in any
other steps deemed reasonably necessary by Discloser to preserve the
confidentiality of any such Confidential Information.
3.3 The foregoing obligations
shall not apply to an item of Confidential Information which Recipient can
establish:
1.was known to Recipient prior to
its receipt from Discloser;
2.is or lawfully becomes
generally known to the public;
3.is lawfully acquired from third
parties who have a right to disclose such information;
4.by mutual agreement is released
from a confidential status; or
5.Recipient is required by law to
release, provided that Discloser is given advance written notice of such
requirement pursuant to Section 3.2.
3.4 All rights in and title to
all Confidential Information will remain with Discloser. Neither the execution and delivery of this
Agreement, nor the performance of the Services, nor the furnishing of any
Confidential Information, will be construed as granting or conferring to
Recipient either expressly, by implication, or otherwise, any license or any
right to use Discloser’s Confidential Information. All materials representing or embodying
Confidential Information that are furnished to Recipient remain the property of
Discloser and, upon termination or expiration of this Agreement or promptly
following Discloser's written request therefor, all such materials, together
with all copies thereof made by or for Recipient, will be returned to Discloser
or, at Discloser's sole discretion, destroyed by Recipient.
3.5 Recipient acknowledges that
the unauthorized use or disclosure of Confidential Information may cause
Discloser irreparable harm and that money damages may be inadequate to
compensate Discloser for such harm.
Accordingly, Recipient agrees that, in addition to any other available
remedies at law or in equity, Discloser will be entitled to seek equitable
relief, including injunctive relief and/or specific performance, the granting
of which shall not be subject to or conditioned upon any requirement of posting
a bond or other security.
SECTION 4 - EXPIRATION OF
AGREEMENT AND TERMINATION
4.1 CUSTOMER acknowledges that
the Remote Assistance Credits expire one (1) year from the effective date of
purchase and that CUSTOMER must utilize the Services available pursuant to this
Agreement prior to that date, unless otherwise agreed in writing. Thereafter, this Agreement and all rights and
obligations hereunder shall terminate and be of no further force or effect.
4.2 Either party may terminate
this Agreement for material breach upon thirty (30) days written notice specifying
the nature of the breach, if such breach has not been cured within the thirty
(30) day period.
SECTION 5 – RELATIONSHIP OF
PARTIES
Each party shall be and act as an
independent contractor and not as partner, joint venturer, or agent of the
other. Neither party has any authority
to act on behalf of or to enter into any contract, incur any liability or make
any representation on behalf of the other party.
SECTION 6 – LIMITATION OF
LIABILITY
TO THE MAXIMUM EXTENT PERMITTED
BY APPLICABLE LAW, IN NO EVENT WILL AREAS BE LIABLE FOR ANY CONSEQUENTIAL,
INDIRECT, EXEMPLARY, SPECIAL, OR INCIDENTAL DAMAGES ARISING FROM OR RELATING TO
THIS AGREEMENT, OR THE SERVICES PERFORMED HEREUNDER. AREAS’S TOTAL CUMULATIVE LIABILITY IN
CONNECTION WITH THIS AGREEMENT, WHETHER IN CONTRACT OR TORT OR OTHERWISE, WILL
NOT EXCEED THE SUM OF THE FEE’S CUSTOMER PAID TO AREAS FOR THE SERVICES GIVING
RISE TO SUCH DAMAGES, NOTWITHSTANDING ANY FAILURE OF ESSENTIAL PURPOSE OF ANY
LIMITED REMEDY.
SECTION 7 - PUBLICITY
AREAS may publicize that CUSTOMER
is a customer of AREAS and use CUSTOMER’s name and logo in connection
therewith.
SECTION 8 - FORCE MAJEURE
Neither CUSTOMER nor AREAS shall
be liable for delays in performing or any failure to perform any of the terms
of this Agreement, except failure to pay amounts properly due, caused by the
effects of fire, flood, strike, war (declared or undeclared), acts of
terrorism, insurrection, government restriction or prohibition, curtailment of
travel, force majeure or other causes reasonably beyond its control and without
its fault, but the party failing to perform shall use commercially reasonable
efforts to resume performance of this Agreement as soon as feasible.
SECTION 9 – INTELLECTUAL PROPERTY
RIGHTS
9.1 Except as otherwise expressly
set forth to the contrary in a written agreement executed by both parties,
neither this Agreement, nor the provision of Services hereunder, shall give
either AREAS or CUSTOMER any ownership interest in or rights to the intellectual
property rights of the other party, and all intellectual property rights that
are owned by a party prior to the commencement of the Services or independently
developed by a party shall remain the property of such party.
9.2 Each party retains all right,
title and interest, including, without limitation, all intellectual property
rights, in and to its Confidential Information.
9.3 AREAS retains all right,
title and interest, including, without limitation, all intellectual property
rights, in and to all intellectual property created in the course of performing
the Services, including all rights in anything delivered in the course of
providing the Services (“Deliverables”), including all works of authorship,
information, data, ideas, designs, inventions, know-how, analytical
methodologies, software, and any other materials authored, invented,
discovered, conceived, reduced to practice, designed, developed, possessed,
owned and/or used under third party license by AREAS as well as any derivative
works.
9.4 CUSTOMER hereby grants to AREAS
a perpetual, royalty-free, irrevocable, world-wide, non-exclusive right and
license, under CUSTOMER’s applicable intellectual property rights, to use in
the performance of Services and in future AREAS products, services and
business, any data, ideas, concepts or know-how related to or arising out of
the material furnished to AREAS in connection with the Services, subject to the
condition that AREAS may not use or re-use any portions of the foregoing in its
future products and services to the extent that such use or re-use would
disclose any Confidential Information belonging to CUSTOMER to any third
parties. CUSTOMER may provide suggestions, comments or other feedback about the
Deliverables, Services or AREAS products (“Feedback”) to AREAS. Both parties agree that all Feedback is and
shall be given entirely voluntarily.
Feedback, even if designated as confidential by CUSTOMER, shall not,
absent a separate written agreement, create any confidentiality obligation for AREAS. AREAS shall be free to use, disclose,
reproduce, license or otherwise distribute, and exploit the Feedback provided
to it as it sees fit, entirely without obligation or restriction of any kind on
account of intellectual property rights or otherwise.
In the absence of any express
license for the Deliverables in a separate document executed by both parties, AREAS
grants to CUSTOMER a limited license to use the Deliverables for its internal
business purposes in conjunction with the Software.

SECTION 11 - COMPLIANCE WITH LAW
Each party will comply with all
statutes, ordinances, and regulations of all Federal, state, county and
municipal or local governments during its performance of its obligations under
this Agreement.
SECTION 12 - MISCELLANEOUS
12.1 The waiver by either party
of a breach or violation of any provision of this Agreement shall not be
construed to be a waiver of any subsequent breach. In the event that any provision of this
Agreement shall be determined to be illegal or unenforceable, that provision
shall be limited or eliminated to the minimum extent necessary so that this
Agreement shall otherwise remain in full force and effect and enforceable.
12.2 Neither party shall have the
right to assign this Agreement or any of the rights or obligations hereunder
without the prior written consent of the other party, except that AREAS may
assign this Agreement to an affiliate or a subsidiary or a successor to that
area of its business to which this Agreement is related.
12.3 This Agreement constitutes
the entire agreement between the parties with respect to the subject matter
herein, and supersedes all prior contracts, agreements and understandings
relating to the same subject matter between the parties.
12.4 Section 2.2 and all
provisions of this Agreement containing license restrictions, warranties and
warranty disclaimers, confidentiality obligations, feedback obligations,
limitations of liability and/or indemnity terms, and any provision of this Agreement
which, by its nature, is intended to survive shall remain in effect following
any termination or expiration of this Agreement.
12.5 The Agreement may not be
amended, nor any provision thereof waived, except by a written amendment
executed by duly authorized representatives of the respective parties
(“Amendment”). In the event of a
conflict between the specific terms and provisions contained in an Amendment
and this Agreement, the terms and provisions of the Amendment shall govern.
12.6 This Agreement shall be
governed by the laws of the Commonwealth of Massachusetts, USA, without regard
to its conflict of laws rules. If any
dispute, controversy, or claim cannot be resolved by such good faith discussion
between the parties, then it shall be submitted for resolution to a state or
Federal court or competent jurisdiction in Massachusetts, USA, and the parties
hereby agree to submit to the jurisdiction and venue of such court.


Instructions
Your aim is to guide the yellow square to the target by programming the path of the yellow square.
The target is invisible but you will be be given either column or row of the target, maximum number of commands you can use and the minimum number of steps you can walk in the bottom of the editor.
Note that the number representing first row and first column is 0. If you succeed, in the next level you will have to navigate to the target through the purple square.
You will be be given either column or row of the target, maximum number of commands you can use and the minimum number of steps you can walk in the bottom of the editor.
You can click on the compass to get your bearing and if you click it again the compass will be hidden. The first number in the bottom of the editor
is the maximum number of commands you can use. The second number is the minimum number of steps you have to walk.
Here are the commands that you can use -
walk(20); - This means that the yellow square will move 20 steps foreward.
turn(N); - This means that the yellow square will move foreward the next time it moves. This is default direction.
turn(E); - This means that the yellow square will move towards right the next time it moves.
turn(S); - This means that the yellow square will move towards backwards the next time it moves.
turn(W); - This means that the yellow square will move towards left the next time it moves.
end() - This marks the end the program.
For example:
walk(3);
turn(E);
walk(4);
end()
This means that the yellow square will move 3 blocks foreward and then turns right walk 4 blocks and
terminates the program. It's your turn now.
For help click the help icon at the bottom of the editor


A Facebook clone with some lil' duckies (because why not, I mean)
Check it outAre you tired about people arguing and whining on Twitter for absolutely no reason? Just use Honker instead, where peace was never an option!
Check it outWorked on the design and implementation of the model and the GUI.
Check it out


Psalms 106:8
Nevertheless he saved them for his name's sake, that he might make his mighty power to be known.


Chaos Cards
Select your character by clicking on one of the images below! If a character has already been chosen it will be greyed out and unavailable


f
mattjamesmedia.com / @mattFrankJames
The average size of a web page has more than tripled over the last seven years.*
From 2013 to 2015, the percentage of people forgoing broadband and relying solely on mobile nearly doubled across all incomes.
Including at..
I remember when 3G was primary mobile speed. It was slow but still worked, so why now when my phone says 3G it becomes completely useless.
— Stephen Gundee (@StephenGundee) March 23, 2016
Averaging 1810kb per page, images make up 56.5% of web bloat
function insertImg() {
const bannerImg = document.querySelectorAll('.heroImg');
if (window.innerWidth >= 750 && (bannerImg.length > 0) && heroFlag === false) {
const imgPath = bannerImg[0].dataset.src;
const hero = document.createElement('img');
hero.src = `${imgPath}.jpg`;
hero.srcset =<EMAIL_ADDRESS>1.5x<EMAIL_ADDRESS>2x`;
hero.alt = ' ';
bannerImg[0].appendChild(hero);
heroFlag = true;
}
}
const gulp = require('gulp');
const imagemin = require('gulp-imagemin');
gulp.task('images', () =>
gulp.src('src/images/*')
.pipe(imagemin([
imagemin.gifsicle({interlaced: true}),
imagemin.jpegtran({progressive: true}),
imagemin.optipng({optimizationLevel: 5}),
imagemin.svgo({plugins: [{removeViewBox: true}]})
]))
.pipe(gulp.dest('dist/images'))
);
const gulp = require('gulp');
const responsive = require('gulp-responsive');
gulp.task('images', () => {
return gulp.src('src/*.{jpg,png}')
.pipe(responsive({
// Resize all JPG images to three different sizes: 200, 500, and 630 pixels
'*.jpg': [{
width: 200,
rename: { suffix: '-200px' },
}, {
width: 500,
rename: { suffix: '-500px' },
}, {
width: 630,
rename: { suffix: '-630px' },
}, {
// Compress, strip metadata, and rename original image
rename: { suffix: '-original' },
}]
}, {
quality: 70,
progressive: true,
withMetadata: false,
}))
.pipe(gulp.dest('dist'));
});
Drop those bad boys in the srcset
atttribute and let the browser decide!
While they aren't a huge slice of the pie, web fonts can have a massive impact on UX
By default @font-face
has a flash of invisible text of up to 3 seconds while your fonts load. On older -webkit- browsers it's unlimited.
With a flash of unstyled text, users get content immediately and your custom fonts swap in when available.
font-display
property in your @font-face
block.
The values block
, swap
, fallback
and optional
tell the browser what to do while the web font downloads.
A sweet polyfill for the CSS Font Loading API from Bram Stein, available via NPM.
const fontA = new FontFaceObserver('Lato');
const fontB = new FontFaceObserver('Montserrat);
Promise.all([fontA.load(), fontB.load()]).then(() => {
document.documentElement.className += " fonts-loaded";
});
.fonts-loaded body {
font-family: 'Lato', sans-serif;
}
.fonts-loaded h1 {
font-family: 'Montserrat', serif;
}
★ A Comprehensive Guide to Font Loading Strategies https://t.co/5LI0HZINku
— Zach Leatherman (@zachleat) July 13, 2016
and how we should handle these render blocking resources
async
and defer
your JS file
Add link rel="preload"
to your CSS
Minify and concatenate your scripts and stylesheets
const gulp = require('gulp'),
useref = require('gulp-useref'),
gulpif = require('gulp-if'),
uglify = require('gulp-uglify'),
cssnano = require('gulp-cssnano');
gulp.task('compress', () =>
gulp.src('app/*.html')
.pipe(useref())
.pipe(gulpif('*.js', uglify()))
.pipe(gulpif('*.css', cssnano()))
.pipe(gulp.dest('dist'))
);
by isolating CSS for above the fold content and inlining it, we dramatically improve perceived performance
const critical = require('critical');
gulp.task('critical', ['build'], function (cb) {
critical.generate({
inline: true,
base: 'dist/',
src: 'index.html',
dest: 'dist/index-critical.html',
minify: true,
width: 320,
height: 480
});
});
Use link rel="preload"
in conjunction with the loadCSS polyfill
What if we were able to minimize almost all network requests after the first visit?
Which assets to cache (static, dynamic, etc)
How to handle page requests
The offline experience
if (navigator.serviceWorker) {
navigator.serviceWorker.register('./serviceworker.js');
}
self.addEventListener('fetch', (event) => {
event.respondWith(
caches.match(event.request)
.then(response => response || fetch(event.request))
);
});
const assets = [
// put your static assets here
];
self.addEventListener('install', (event) => {
self.skipWaiting();
event.waitUntil(
caches.open('static-v6')
.then(cache => cache.addAll(assets))
);
});
const expectedCaches = [
'static-v6',
];
self.addEventListener('activate', (event) => {
self.clients.claim();
event.waitUntil(
caches.keys().then((cacheNames) => {
return Promise.all(
cacheNames.map((cacheName) => {
if (!expectedCaches.includes(cacheName)) {
return caches.delete(cacheName);
}
})
);
})
);
});
| Before | After | |
|---|---|---|
| Cable | 4.2 sec | 3.4 sec |
| 3G | 10.5 sec | 6.4 sec |
| 2G | 31.91 sec | 14.63 sec |
Identify which resources are causing hangups
A recent report from Google subsidiary DoubleClick found 53% of mobile users abandon sites that take longer than 3 seconds to load.
- https://glen-purchase.glitch.me
- Source code on Github
- @mattFrankJames
- mattjamesmedia.com
- #fastsite 5 Hot Lightning!


about
Jacqueline is a Vancouverite transplanted in Toronto with a love of yoga, gluten-free goodies, painted toenails, public libraries, un-tucked hotel sheets, sunshine, heart-opening back bends, Old Spice, and epic walks around the city. While managing both a corporate job and teaching yoga, she finds balance on and off her mat.
Jacqueline discovered yoga at a retreat in Greece in 2007 where she fell in love with the practice. She hasn’t looked back since. After trekking all over the world with her mat slung over her shoulder, she landed in the wonderful city of Toronto. Jackie completed her teacher training at Kula Annex and is grateful for her teachers Christi-an Slomka and Lawrence-Jacob Milman. She has also been fortunate to study with Ryan Leier, Dina Tsouluhas, and Dustin Fruson. She will forever be a student of yoga, searching out formal training, while learning the most from her students and her personal practice.
Jacqueline keeps her yoga classes fun-filled so her students can feel comfortable approaching their yoga practice with a sense of curiosity, compassion, freedom, playfulness, and courage. Weaving creativity into each class, she looks and finds the beauty in everyone.
She also loves to play music – you’ll be guaranteed to hear some Lauren Hill, Madonna, or Florence + the Machine.
contact
requests
"There are three ways to be in the world: 1. To believe that we are each alone and separate from one another. 2. To realize that we are all connected. And 3. To realize that we are all one." Ram Dass
schedule
Please contact Jacqueline to find out about private sessions, corporate classes, and karma classes (she is always looking to give back through the practice)


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| Listed Size | Waist | Inseam | Front Rise | |
|---|---|---|---|---|
| {{eachSize['Listed Size']}} | {{eachSize['Waist']}} | {{eachSize['Inseam']}} | {{eachSize['Front Rise']}} | |
|
NightWalker believes very strongly in accurate sizing information. We do not practice "vanity sizing", so the measurements here are the actual, measured sizes |
||||
| Listed Size | Thigh | Knee | Leg Opening | |
| {{eachSize['Listed Size']}} | {{eachSize['Thigh']}} | {{eachSize['Knee']}} | {{eachSize['Leg Opening']}} |


The OSCAR Lab is an inter-disciplinary computer-systems research group. We explore opportunities in the entire computing stack including, but not limited to: Operating Systems, Virtualization, System Security, Concurrency, Compilers, and Computer Architecture.


Either the well was very deep or she fell very slowly, for she had plenty of time to look about her on the way down.
At first it was too dark for her to see anything, but as her eyes adjusted, she could make out what seemed to be cupboards, bookshelves, paintings, lining the walls she was rushing past.
Down, down, down she fell. Would it never come to an end? She began to wonder how many miles she’d fallen and whether she’d end up at the center of the Earth—or come out the other side!
How would she call her sister? What if she didn’t speak the language? What if they couldn’t understand her and put her in an orphanage? She’d never see her sister or her cat again!
Poor Tuna! Would they remember to clean his litter box or give him his kibble? If only Tuna were with her now. There were no mice to live on in the air, but perhaps he could catch a bat.
Before she could worry too much, her eyes grew heavy and she began to nod off whispering, “Do cats eat bats?” to no one in particular.
She must’ve been dreaming, because for a moment she imagined he was there with her, as large as her sister.