What is the right way to treate "Optimization terminated: no feasible point found." ???

What is the right way to treate "Optimization terminated: no feasible point found." ???

[Matt J]

In what code?

[Walter Roberson]

Check that your constraints can be met; if the optimizer permits an initial value then try to give it one that is within constraints.

Answer 1

[John D'Errico]

A feasible solution is some point, ANY point, that satisfies all of the constraints. There is no assurance that a feasible point exists at all for some unknown problem. However, the odds are decent that no such solution exists if MATLAB returns that statement. But we can never know that, given no information at all.

So it depends. Are your constraints highly nonlinear? Just how nasty are they? Have you even implemented them in MATLAB properly? We cannot know this, given no code to see at all, and no information as to the problem you want to solve.

So what should you do?

1. Verify the implementation. CAREFULLY. Do this twice. Then do it a third time. Get a colleague to check your implementation.

2. Try some random points. Do the constraints make sense in what they return? APPLY COMMON SENSE. Do any of them satisfy all of the constraints? MATLAB starts looking for a feasible point based on your initial values. So a different start point might allow the solver to find a feasible point.

If it appears that no feasible point seems to be found, then consider if there is a good reason why no feasible point was found.

Of course, if you want better help, you would have more of a chance if you explain what you are trying to solve, and showed what you did. Otherwise, it is difficult to utter more than the vague platitudes that I did here.

[Walter Roberson]

Note that especially in the case of highly non-linear functions, sometimes MATLAB is able to detect that a zero crossing exists but is unable to determine where the crossing is, because the values for the last representable number on each side of the crossing might be quite distant from 0. For example even something as simple as 1E100*x cannot have its zero crossing determined with enough precision for some of the routines to satisfy their tolerances.

Answer 2

[Alan Weiss]

In addition to what John and Walter said, the documentation has some suggestions on how to search for a feasible point.

Good luck,

Alan Weiss

MATLAB mathematical toolbox documentation

Answer 3

[tahseen alshmary]

i have this problem appear with genetic algorithm ,what is the solution

[John D'Errico]

Why would you answer a question with an answer that is just a question in itself? And why did you not read the answers already provided, since they give guidance on what to do?

A lack of a feasible point found might indicate you screwed up in one of a variety of ways, or it might indicate no solution exists. So READ THE ANSWERS ALREADY PROVIDED. Think about what you read there.

[Walter Roberson]

We described the solutions above: check your code and recheck it for mistakes. Work through the geometry of your linear constraints to see if there is any region in which they can be simultaneously satisfied. If you have nonlinear constraints, it might be difficult to find any point that satisfies the condition even if you know that mathematically that some points exist.

If you know of one or more points that satisfy the conditions, then use your options to set an initial population matrix.

[tahseen alshmary]

thanks a lot about your answer . Do you have genetic algorithm code , may i get it please

[Walter Roberson]

Mathworks provides ga() and gamultiobj() in the Global Optimization Toolbox.

There are over 50 genetic algorithm implementations in the File Exchange: https://www.mathworks.com/matlabcentral/fileexchange/?utf8=%E2%9C%93&term=tag%3Aga