MATLAB: Using Simulated Annealing along with Nonlinear Constraints

simulannealbnd simulated annealing optimization minimization

MATHWORKS, Thanks for all your help in the past. I'm trying to find the global minimum for a highly non-linear set of equations (minimizing Gibbs free energy). The book that I'm studying recommends simulated annealing. The MATLAB simulated annealing function (simulannealbnd) works great when I use scalar upper and lower bounds for x (i.e. lb = [0.001 0.001] ub = [0.999 0.999]). Yet, the problem is that my x vector must sum to 1. Do you know if there's a way to do this? Maybe can I have simulannealbnd accept function constraints? Or is it possible to minimize two function? One for Gibbs energy and the other for y = 1 – x(1) – x(2). Thanks so much for your help!

Best Answer

Optimize on one variable with lower bound eps and upper bound 1-eps. Inside the function define x2 as 1-eps

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MATLAB: Problem regarding fmincon solver

lb = [0.0;0.0;0.0;2.0;1.0];
ub = [4.0;4.0;3.0;4.0;12.0];
x0 = [0.0,0.0,0.0,0.0,0.0];

lb is lower bounds. Your second-last entry in lb is 2.0, so the second last entry in x0 must be at least 2.0, as in

x0 = [0.0,0.0,0.0,2.0,1.0];

MATLAB: Modify the given vector x=[1.1 1.4 1.9 2.1 1.3 1.7 1.8 5 1.5] by elimination of values that differ by more than the standard deviation from the mean value of the given sequence.

Feeling loopy tonight, but not that loopy!

Try this, avoiding the loopys:

x=[1.1 1.4 1.9 2.1 1.3 1.7 1.8 5 1.5];
x_mean = mean(x);
x_stdv = std(x);
limits = [-x_stdv  x_stdv]+x_mean;
x_dev = (x<=limits(1)) | (x>=limits(2))             % Logical Vector wheren ‘1’ (True) Meets The Criteria And ‘0’ (False) Fails To  Meet It                                                     
_dev =
    1×9 logical array
     0   0   0   0   0   0   0   1   0

Best Answer

Related Solutions

MATLAB: Problem regarding fmincon solver

MATLAB: Modify the given vector x=[1.1 1.4 1.9 2.1 1.3 1.7 1.8 5 1.5] by elimination of values that differ by more than the standard deviation from the mean value of the given sequence.

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