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I'm working for quite some time on finding a numerical instance for solution of a 8 variables system of 7 very complicated inequalities plus region specification. Unfortunately I cannot produce a MWE or nothing of the sort since the inputs are really long.

My current method is Mathematica's NMinimize routine, minimizing one of the 7 inequalities subject to every other condition as constraint -- The FindInstance command simply quits the kernel without being able to finish running.

The NMinimize is able to produce output, but besides being slower than would be optimal, produce results that do not obey every constraint.

The thing is that I need to be certain, for each benchmark I run, that if the output doesn't satisfy every constraint it is because such a set of real numbers doesn't exist -- which with my current method I can't be, by experience.

So: is there a foolproof, as efficient as possible, computational method for me to find a single instance of numerical solution to 7 complicated inequalities (involving trigonometric functions) of 8 variables or be sure that such a set doesn't exist?

It could be a Mathematica/python/fortran package, genetic algorithm or anything -- as long as there is clear enough documentation.

GaloisFan
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You need to give importance multiplier to constraints and the optimization method should not be greedy.

A genetic algorithm combined with multiple-starting points (or simulated annealing for diminishing mutations) tends to converge to global minima (hence not greedy) with more time given to it but there is not guarantee that the heuristic will complete X function in Y time. The more time given to it, the better it converges to global minima.

In genetic algorithm, you can add big constraint penalties like this:

fitness_minima = some_function_output_between_1_and_10 + 
                 constraints_breached?1000.0f:0;

so that the DNAs with no contraint-violations will be favored for the crossover part of GA.

"As efficient as possible" depends on your algorithm. If you can parallelize the algorithm and run it on multiple GPUs, it should give substantial speedup over CPU. Compared to some hours of Mona-Lisa painting by CPU, a parallelized version running on 3 low-end GPUs complete within 10 minutes (https://www.youtube.com/watch?v=QRZqBLJ6brQ). At least some OpenCL/CUDA supporting libraries/frameworks (like Tensorflow) should be able to accelerate your algorithm if you don't want to do the work distribution yourself.

huseyin tugrul buyukisik
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