Recently, it has been recognized that phase transitions play an important role in the probabilistic analysis of combinatorial optimization problems. However, there are in fact many other relations that lead to close ties between computer science and statistical physics. This review aims at presenting the tools and concepts designed by physicists to deal with optimization or decision problems in...

The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: warm start initialization, active set updating, and strong rule for coordinate preselection. Such a complex algorithmic structure grants superior ...

The pathwise coordinate optimization is one of the most important computational frameworks for solving high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: warm start initialization, active set updating, and strong rule for coordinate preselection. These three features grant superior empirical...

In a stochastic simulation context, iterative methods of optimization, which perform at each step of their optimization procedure a comparison between two di€erent values of the objective function, need the use of statistical tests in order to properly evaluate and compare the simulation results. However, when the objective function to be optimized is a multicriteria function involving several ...

Recently, it has been recognized that phase transitions play an important role in the probabilistic analysis of combinatorial optimization problems. However, there are in fact many other relations that lead to close ties between computer science and statistical physics. This review aims at presenting the tools and concepts designed by physicists to deal with optimization or decision problems in...