This paper presents a set of capping methods to speed-up the automated configuration of optimization algorithms. These methods use known previous executions to compute a performance envelope, which is used to evaluate new executions and early stop those with unsatisfactory performance. Preliminary experiments on six scenarios show that the capping methods save up to 78% of the configuration effort, while finding configurations of the same quality
This article aims at making iterative optimization practical and usable by speeding up the evaluatio...
Automated algorithm configuration has been proven to be an effective approach for achieving improved...
Compile-time optimizations generally improve program performance. Nevertheless, degradations caused ...
Automatic configuration techniques are widely and successfully used to find good parameter settings ...
The best-performing algorithms for many hard problems are highly parameterized. Selecting the best h...
The performance of many hard combinatorial problem solvers depends strongly on their parameter setti...
This paper presents an automated performance tuning solution, which partitions a program into a numb...
The design and configuration of optimization algorithms for computationally hard problems is a time-...
The best-performing algorithms for many hard problems are highly parameterized. Selecting the best h...
Algorithms for solving hard optimization problems usually have a number of parameters that greatly i...
Compile-time optimizations generally improve program performance. Nevertheless, degradations caused ...
This article aims at making iterative optimization practical and usable by speeding up the evaluatio...
Although compile-time optimizations generally improve program performance, degradations caused by in...
This article aims at making iterative optimization practical and usable by speeding up the evaluatio...
Automatic algorithm configuration techniques have proved to be successful in finding performance-opt...
This article aims at making iterative optimization practical and usable by speeding up the evaluatio...
Automated algorithm configuration has been proven to be an effective approach for achieving improved...
Compile-time optimizations generally improve program performance. Nevertheless, degradations caused ...
Automatic configuration techniques are widely and successfully used to find good parameter settings ...
The best-performing algorithms for many hard problems are highly parameterized. Selecting the best h...
The performance of many hard combinatorial problem solvers depends strongly on their parameter setti...
This paper presents an automated performance tuning solution, which partitions a program into a numb...
The design and configuration of optimization algorithms for computationally hard problems is a time-...
The best-performing algorithms for many hard problems are highly parameterized. Selecting the best h...
Algorithms for solving hard optimization problems usually have a number of parameters that greatly i...
Compile-time optimizations generally improve program performance. Nevertheless, degradations caused ...
This article aims at making iterative optimization practical and usable by speeding up the evaluatio...
Although compile-time optimizations generally improve program performance, degradations caused by in...
This article aims at making iterative optimization practical and usable by speeding up the evaluatio...
Automatic algorithm configuration techniques have proved to be successful in finding performance-opt...
This article aims at making iterative optimization practical and usable by speeding up the evaluatio...
Automated algorithm configuration has been proven to be an effective approach for achieving improved...
Compile-time optimizations generally improve program performance. Nevertheless, degradations caused ...