Add to ORKGInternational audienceWe propose a methodology, based on machine learning and optimization, for selecting a solver configuration for a given instance. First, we employ a set of solved instances and configurations in order to learn a performance function of the solver. Secondly, we solve a mixed-integer nonlinear program in order to find the best algorithmic configuration based on the performance function
Automated algorithm configuration has been proven to be an effective approach for achieving improved...
The performance of algorithms is often highly sensitive to the values of their pa rameters. Therefor...
The performance of many hard combinatorial problem solvers depends strongly on their parameter setti...
We propose a methodology, based on machine learning and optimization, for selecting a solver configu...
International audienceWe propose a methodology, based on machine learning and optimization, for sele...
International audienceWe propose a methodology, based on machine learning and optimization, for sele...
We discuss the issue of finding a good mathematical programming solver configuration for a particula...
The research topics of this Ph.D. thesis lie at the intersection of Machine Learning (ML) and Mathem...
The best-performing algorithms for many hard problems are highly parameterized. Selecting the best h...
The research topics described in this Ph.D. thesis lie at the intersection of Machine Learning (ML) ...
The best-performing algorithms for many hard problems are highly parameterized. Selecting the best h...
We present a problem class of mixed-integer nonlinear programs (MINLPs) with nonconvex continuous re...
Mixed Integer Linear Programs (MILP) are well known to be NP-hard (Non-deterministic Polynomial-time...
This paper addresses the problem of tuning parameters of mathematical solvers to increase their perf...
Algorithm designers are regularly faced with the tedious task of finding suitable default values fo...
Automated algorithm configuration has been proven to be an effective approach for achieving improved...
The performance of algorithms is often highly sensitive to the values of their pa rameters. Therefor...
The performance of many hard combinatorial problem solvers depends strongly on their parameter setti...
We propose a methodology, based on machine learning and optimization, for selecting a solver configu...
International audienceWe propose a methodology, based on machine learning and optimization, for sele...
International audienceWe propose a methodology, based on machine learning and optimization, for sele...
We discuss the issue of finding a good mathematical programming solver configuration for a particula...
The research topics of this Ph.D. thesis lie at the intersection of Machine Learning (ML) and Mathem...
The best-performing algorithms for many hard problems are highly parameterized. Selecting the best h...
The research topics described in this Ph.D. thesis lie at the intersection of Machine Learning (ML) ...
The best-performing algorithms for many hard problems are highly parameterized. Selecting the best h...
We present a problem class of mixed-integer nonlinear programs (MINLPs) with nonconvex continuous re...
Mixed Integer Linear Programs (MILP) are well known to be NP-hard (Non-deterministic Polynomial-time...
This paper addresses the problem of tuning parameters of mathematical solvers to increase their perf...
Algorithm designers are regularly faced with the tedious task of finding suitable default values fo...
Automated algorithm configuration has been proven to be an effective approach for achieving improved...
The performance of algorithms is often highly sensitive to the values of their pa rameters. Therefor...
The performance of many hard combinatorial problem solvers depends strongly on their parameter setti...