Local search: uses (good and bad)

Some time ago @lambdadmitry asked me to elaborate  a bit about some discussion about the relevance of local search, which originated from a tweet by @JeffLinderoth. I apologize that it took me so long to complete this post, but I found some additional wisdom in yesterday’s talk by @MikeTrick. I will provide some examples of known problems. […]

Summarizing the Metaheuristics International Conference 2013 in three words

This year’s Metaheuristics International Conference (MIC2013) has been held in Singapore. Around 70 presentations were made during the conference. In this post, I provide a short overview of what has been discussed through three keywords: hybridization, extensions and applications.

Experimenting with LocalSolver 3.0 [primal]

This post presents and discusses results of some experiments performed with LocalSolver 3.0, a solver based on the local search paradigm. I investigate whether LS is able to find good solutions quickly and reliably. This post is a follow-up of my first experiments with LocalSolver; I encourage you to read this if you didn’t do so already. The […]

Testing a solver based on local search [dual]

This post discusses some challenges associated with testing a solver based on local search heuristics. Local search is quite different from mathematical programming-based solvers and it should be tested accordingly. The associated primal post (not yet published) presents the results of my experiments with LocalSolver 3.0. Local Search VS MI(N)LP: Apples and Oranges First thing […]

Speed dating with LocalSolver

This post records my observations and comments after my first evening using LocalSolver, a solver based on the local search paradigm rather than on the branch-and-cut framework common to most MIP solvers. Overall, my first contact with LocalSolver was quite positive. This is a very general review, as its performance and features will be reviewed in […]

Proclaiming the failure of solvers

Designing specialized optimization algorithms to solve challenging optimization problems has been a very popular research area among academics. Yet much of the justification for this kind of research lies on the inhability of solvers to solve a particular problem. This is fine. But sometimes, we are a bit unfair to solvers, and most of all, […]

Closing the (MIP) gap – Part I

MIP Solvers are more powerful than ever. They mainly do two things: (1) find high-quality (optimal) solutions, and (2) proving the optimality of that solution. While in most cases performance is evaluated in terms of time-to-optimality, quite often a high quality solution is found in in only a fraction of the time needed to prove […]

The heuristic ‘feel’ of MIP solvers

MIP solvers are now more powerful than ever. Models that were considered very difficult 10 years ago are now routine work. They are solved efficiently, and performance is pretty stable from one instance to another. I also like to use solvers on more difficult models and instances. On large classes of models, you still get a very […]