Matlab optimization module problem
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I am using matlab R2016a. I am using optimization module for my application
trying to find the minimum in my function (below is the main optimization
lines). There are two variables that I'm interested to optimize them to
make the function minimum. The objective function has lots of local
minimums. Unfortunately each run gave me different variables. Sometimes
there's 30% or more difference between runs results. And sometimes the
optimization module get stuck in one of the local minimums. I tried to tune
every tolerance and step size but no success so far and I don't get a
consistent final results for my two variables. Could you please help me how
can I tune the Options to have a quicker and consistent result. ; options =
optimoptions('fmincon','Display','iter','Algorithm','
sqp','MaxFunctionEvaluations',1500,'MaxIterations',1500,'
ConstraintTolerance',1.0e-10,'OptimalityTolerance',1.0e-10,'StepTolerance',1.0e-10);;
estparam=fmincon(@objfun,estparam0,A,b,Aeq,beq,lb,ub,[],options)
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MathWorks Support Team
2023-5-17
编辑：MathWorks Support Team
2023-5-17
0 个投票
If you have access to the Global Optimization Toolbox, you may use “GlobalSearch” or “MultiStart” features of the toolbox to obtain the global minimum to your problem.
In addition, another potential workaround is to use the function “patternsearch”
Example:
>> options = optimoptions('patternsearch','Display','iter','MaxFunctionEvaluations',1500,'MaxIterations',1500,'ConstraintTolerance',1.0e-10,'StepTolerance',1.0e-10);
>> estparam=patternsearch(@objfun,estparam0,A,b,Aeq,beq,lb,ub,[],options)
Finally, you may also try calling “fmincon” multiple times in a “for” or “parfor” loop, giving different initial guesses each time and keeping track of the best solution. This will not necessarily guarantee a global minimum, but it will likely yield a better and more consistent answer.
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2016-11-30
2023-5-17
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