22

"General purpose optimization" is quite broad, so I'll take a step back first, to better identifying the motivation of using ML in optimization settings. To keep things simple, I'll consider a single-objective minimization problem with decision vector $x$, objective function $f$ and some constraints $x \in X$, i.e., \begin{align} (P) \ \ \ \min_{x} ...


19

Regarding the paper, it's important to remember that general purpose MIP solvers are meant to be general purpose, hence it's not surprising that they can be improved by tailoring them to the test set, either using ML or some other form of automatic tuning. MIP solvers make many decisions while solving a problem and I guess it's quite natural to assume that ...


17

The following is largely opinion/conjecture on my part. Many (though not all) heuristics involve neighborhood search. For that type of heuristic to be effective, you need "neighborhood" to be defined in a way that is both computationally convenient (moving from one solution to a "neighboring" solution is straightforward and does not ...


11

SCIP is not slow. SCIP's code is roughly as fast as the commercial alternatives. What makes SCIP seem slower to the user is that, by comparison, the commercial solver heuristics (cuts, primal heuristics, branching, tuning) are superior. Therefore, that paper actually makes a very sound comparison: "What if we had a machine figure out the heuristics ...


8

Has anyone tested this approach on a real world business problem? If the question is, more generally, "for a practical optimization problem, can ML somehow accelerate the performance of a state-of-the-art MIP solver, given that we have already solved a large number of similar instances in the past?", then the answer is yes. In the reference below, ...


6

I'm going to interpret "in OR" as appearing in OR journals and/or written by people who identify as OR/MS/IE researchers. I'm a bit familiar with the intersection of optimization and statistical estimation. Machine learning, OLS and LAV regression, lasso regression etc. all rely on solving optimization problems to fit models. In addition, ...


6

tl;dr– Having relevant knowledge can help folks come up with better techniques, including heuristic techniques. Relevant knowledge can be useful in problem-solving. Heuristic techniques are those that're somehow fuzzy/approximate/unreliable. The term "heuristic" is basically a disclaimer, qualifying that a technique isn't ideal. Relevant ...


4

Here is a workshop that was held today. It was on Machine Learning and Optimization. Link to some speakers and their profiles are provided. Some of their works revolve around that idea. https://abs.uva.nl/shared/subsites/optimization-for-and-with-machine-learning-optimal/en/events/events/2021/09/machine-learning-for-optimization-workshop.html?origin=...


3

Heuristics are "rules of thumb" that in some cases try to estimate some quantity without explicitly computing it. The better the estimate, the better the algorithmic performance, so good heuristics are important. Domain knowledge can help to improve estimates by understanding what is possible or reasonable. Take A* search, for example, which can be ...


1

This talk discusses several approaches to integrate machine learning in local search algorithms by identifying good solutions bad solutions promising neighborhoods through offline learning of problem instance and solution features. This paper looks at the frequency of good (partial) paths of locally optimal solutions during runtime. Good partial solutions ...


1

Many people seem to think that, but we do nothing of the sort in OR - our process is much simpler than people think. We build and solve a model, and if the results make sense, we are pretty much done. If the results don't make sense we typically know why. Crucially, we can know whether the issue is in the data or not. The reason OR has a reputation for being ...


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