# Tag Info

Accepted

### What global MINLP solvers support trigonometric functions?

SCIP does not currently support any trigonometric functions as of this post from May 2018. COUENNE appears to handle $\sin$ and $\cos$ expressions. ANTIGONE appears to not support any trigonometric ...
Accepted

### Simulation optimisation: Monte carlo simulation, regression, optimise within regression model?

After further reading it seems that "surrogate models" do exactly this (aka. "meta-models"), a recent discussion of methods is here: https://onlinelibrary.wiley.com/doi/full/10.1111/itor.12292 Also ...

### What global MINLP solvers support trigonometric functions?

Here is an option for MATLAB users which is not mentioned in any other answers. YALMIP's BMIBNB iI a global branch and bound solver https://yalmip.github.io/solver/bmibnb/. It is used in combination ...
Accepted

### Forbid transformation of max(x,y) into MILP

In YALMIP you can use arbitrary black-box operators to circumvent modelling ...
Accepted

### Finding the global minimum of $f(\mathbf{x})=\|(1-x_1,x_1-x_2,x_2-x_3,\ldots,x_{n-1}-x_n,x_n-2)\|_2^2$

Cross-posting. The math looks good but why starting with $\nabla f(x) = 0$ instead of $\frac{1}{2}\nabla f(x) = 0$? You should have noticed that the factor $2$ was everywhere and could be simplified. ...

### Heuristic methods for optimising complex black box function over permutations/ranks?

An approach that is easy to implement and always worth a shot is a simple neighborhood search, where you start with some candidate solution and iteratively move to a neighbor solution. For your ...

### Simulation optimisation: Monte carlo simulation, regression, optimise within regression model?

You might want to google "response surface methodology" and "simulation" together. There is a ton of literature on this, a mixture of how do to response surface modeling (and how to use it to optimize ...

### What global MINLP solvers support trigonometric functions?

From the presentation given by Vigerske, S. (2015), it is noted that the solvers Couenne by P. Belotti and LindoAPI by Y. Lin and L. Schrage can handle trigonometric expressions. Both are ...
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### Black-box optimization of a single parameter function with high cost evaluation

Given that your function is apparently unimodal (single local minimum, which is global), you might try golden section search. The first four function evaluations result in about a 40% reduction in the ...

### What global MINLP solvers support trigonometric functions?

Octeract Engine (the solver I develop), is a deterministic global MINLP solver that supports all trigonometric functions (including hyperbolic functions).

### On the global optimization solver Octeract

Thanks for your interest. We discontinued the free version in 2022 due to various reasons, and the solver has been a paid product for all audiences ever since. Our Neural service is free, but it's ...
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### Global Optimization when the exponential function is involved

This is a non-convex global optimisation problem. The state-of-the-art way to solve this is to use a factorable relaxation. A key insight here is that $e^{-\alpha X}$ is convex (since your $\alpha$ is ...

### Global Optimization when the exponential function is involved

Assuming the continuous relaxation is convex you can most likely use conic optimization with the exponential cone. The Mosek modelling cookbook has the details. Unsurprisingly Mosek can solve the ...

### General Optimization and Unsolvability

General global optimisation problems are solvable in finite time and in a finite number of steps, under the following assumptions: All mathematical expressions are available (i.e. not black-box ...

### Prove NP Hardness for non-convex multi-objective optimization

The notion of NP-hardness relates to whether one class of problems can be solved by a solver for another class of problems where the translation overhead is negligible. The problem you presented is ...

### Forbid transformation of max(x,y) into MILP

The default Octeract Engine behaviour is to handle this automatically without reformulating to an MILP.
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### How to prove that optimizing each component of a system separately gives suboptimal result?

Method #1 is called local optimization, while Method #2 is called global optimization. In practice, all modelling is local. That is, we have to draw a boundary around the parts of the whole system ...

### Prove NP Hardness for non-convex multi-objective optimization

NP-hardness is an asymptotic result of increasing problem dimensions. It is not a property of one fixed problem instance. So to ask whether your problem class is NP-hard, you would have to explain how ...

### Transportation problem with consolidation within path

If your demands are per source-destination pairs, then you are very likely dealing with a variation of the multi-commodity flow problem, where each source-destination pair is a commodity. The ...

### How to prove that optimizing each component of a system separately gives suboptimal result?

To add the answer by @solver max (I think it is a very good answer), Please be aware that, neither method-$1$ nor method-$2$ has a preference over another. Specifically, for the black-box optimization,...

### Heuristic methods for optimising complex black box function over permutations/ranks?

A "random key" genetic algorithm (henceforth RKGA) might get you a pretty good solution within your function call limit (particularly if you cache function values, so that repeated evaluations of the ...

### Global optimality condition of non-convex quadratic programs

In addition to the references already given in the comments, this paper (DOI link) demonstrates that exact solutions to some non-convex quadratic programs are given by semi-definite programming, and ...

### Piecewise linear and global optimization

I might be missing something here (and by "might be" I mean "probably am"), but at least for the case of $x_i \in [0,1]$ you might be able to get solutions via "brute force&...
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### Piecewise linear and global optimization

As far as I can see there is no exact convex reformulation for this, unless someone else can think of a nice trick. Constraint 1 can actually be convexified for certain ranges of $q_i$ as these ...

### Global optimality condition of non-convex quadratic programs

With the exception of special cases, this problem is NP-hard. One interesting case is that minimizing a convex or concave function over a simplex can be solved in polynomial time. In other special ...

### Global Optimization when the exponential function is involved

You may find a partial answer to your question in the following article (forthcoming in OR) by JP Vielma and J Huchette https://arxiv.org/abs/1708.00050 In that paper, the authors consider the problem ...

### Black-box optimization of a single parameter function with high cost evaluation

I would advise Bayesian Optimization. The benefits imho are that they don’t require a gradient, work for a wide variety of optimization problems and are made for when we are dealing with functions ...

### General Optimization and Unsolvability

I had a look at your paper. Here are two errors that I think are worth reflecting about: You are assuming computations in finite precision, and consider solving a continous optimization problem. We ...
If $g_{oracle}(x)$ can return gradients, then this is a standard nonlinear optimization problem which can be passed directly to an oracle-based solver like Ipopt (https://github.com/coin-or/Ipopt). ...