[Top][All Lists]
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
[Help-glpk] Use of LP for experimental trials
From: |
dhdoyle |
Subject: |
[Help-glpk] Use of LP for experimental trials |
Date: |
Wed, 19 Apr 2006 21:59:44 +0359 |
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
- [Help-glpk] Use of LP for experimental trials,
dhdoyle <=