Combinatorial optimization algorithms

We develop algorithms for tackling combinatorial optimization problems, and analyze their properties and relative merits. They are then sometimes used to understand the statistical properties of the optimum, low cost solutions, or energy landscapes.


Local search, simulated annealing, iterated local search, genetic algorithms, renormalization, multi-level, parallel and distributed computation, PVM.

Systems investigated:

Traveling salesman problems, graph bipartitioning problems, max-cut (spin glasses).

Publications in reverse chronological order:

LPTMS Home Page - IPN Home Page - Olivier MARTIN Home Page

Last modified: May 2006
in anti spam format: olivier a-dot-here martin (at sign) u-psud a-dot-there fr