Hi Gams world,

I have a convex minlp model and it is very difficult to solve even though the model size is not very large (in total 238 binary variables and 136 continuous variables), and I am sure that the model is convex, the only nonlinear inequalities are:

x**1.85 + A*(1-y) + B =l= 0 ;

where x is positive variable, y is binary variable, A and B are parameters.

The model becomes very easy to solve if the inequalities become linear:

x + A*(1-y) + B =l= 0 ;

Therefore I guess the difficulty is not coming from the combination of binary variables.

On the other hand, the relaxed minlp problem is also very easy to solve since it is convex, which makes me wonder why it the so hard to solve this minlp problem.

Is there any efficient solver for convex minlp problem? Or is there any way to reformulate the nonlinear inequalities to make it easier to solve?

Besides, I am using BARON to solve a convex MINLP problem. While solving the problem, the solver suggests the memory requirement exceeds its estimation and request to provide more memory.

Later I tried the GAMS option ‘workspace’ and ‘workfactor’:

option workspace = 30 ;

However, both are recognized as invalid. How can I provide more memory to the solver?

Best regards,

Dylan

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