[Help-glpk] Use of LP for experimental trials

[dhdoyle]

Hi,

 

I'd like to know if 
I can use glpk to solve a problem where the solver in glpk would call a 
function.  I'm an engineer who no longer does any real coding.  I used 
simplex methods years back with good success on several types of problems.  
I'm working to determine the best set of test conditions for a semiconductor 
chip.  These correspond to internal voltage levels and delays.  They 
are set as bits.  There are a total of 55 bits, in 20 variables for 3E16 
combinations.  I'd like to try a simplex type algo that runs a point or 
trial in X dimensional space, evaluates the results and selects the vectors for 
the next trial.  Each trial takes 5 minutes.  

 

So the problem 
is:

 

1) Must be evaluated 
as an external function with the values passed in.  The return is a value 
to be minimized.  In other words, there are no constraints or known 
equation for the function.

2) Should be 
efficient in trials, due to the 5 min per iteration (thus my interest in 
Simplex 
over a genetic code)

3) I have to be able 
to convert it to integer points

4) Each variable has 
upper and lower limits expressed as 0-7 or 0-15.

5) The space has 
local minima, so if it can bounce out great.  However, I am just looking 
for improvement over the current "optimum" not a global 
solution.

6) I don't need to 
evaluate the whole space at once, I can fix X variables and look for 
improvements in the settings of 20-X variables.

7) A PERL shell 
would set up the seed and track outputs.  The glpk would be called and in 
turn call the function which is really a chip tester.

8) The current 
method employed is full matrix search on 1-3 variables at a 
time.

 

Can glpk work for 
this problem?  If so which modules should I try and what mods to the code 
do I need if any?  Is there other software out there better suited to this 
application?

Thanks in advance,

Dan

Hi,

 

I'd like to know if I can use glpk to solve a problem where the solver in glpk would call a function.  I'm an engineer who no longer does any real coding.  I used simplex methods years back with good success on several types of problems.  I'm working to determine the best set of test conditions for a semiconductor chip.  These correspond to internal voltage levels and delays.  They are set as bits.  There are a total of 55 bits, in 20 variables for 3E16 combinations.  I'd like to try a simplex type algo that runs a point or trial in X dimensional space, evaluates the results and selects the vectors for the next trial.  Each trial takes 5 minutes. 

 

So the problem is:

 

1) Must be evaluated as an external function with the values passed in.  The return is a value to be minimized.  In other words, there are no constraints or known equation for the function.

2) Should be efficient in trials, due to the 5 min per iteration (thus my interest in Simplex over a genetic code)

3) I have to be able to convert it to integer points

4) Each variable has upper and lower limits expressed as 0-7 or 0-15.

5) The space has local minima, so if it can bounce out great.  However, I am just looking for improvement over the current "optimum" not a global solution.

6) I don't need to evaluate the whole space at once, I can fix X variables and look for improvements in the settings of 20-X variables.

7) A PERL shell would set up the seed and track outputs.  The glpk would be called and in turn call the function which is really a chip tester.

8) The current method employed is full matrix search on 1-3 variables at a time.

 

Can glpk work for this problem?  If so which modules should I try and what mods to the code do I need if any?  Is there other software out there better suited to this application?

Thanks in advance,

Dan