[PDF][PDF] Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering.
Different solution approaches for combinatorial problems often exhibit incomparable
performance that depends on the concrete problem instance to be solved. Algorithm …
performance that depends on the concrete problem instance to be solved. Algorithm …
Predicate logic as a modeling language: the IDP system
Since the early days of artificial intelligence, it has been believed that logic could bring
important benefits in solving computational problems and tasks compared to standard …
important benefits in solving computational problems and tasks compared to standard …
SUNNY: a lazy portfolio approach for constraint solving
Within the context of constraint solving, a portfolio approach allows one to exploit the
synergy between different solvers in order to create a globally better solver. In this paper we …
synergy between different solvers in order to create a globally better solver. In this paper we …
Towards effective deep learning for constraint satisfaction problems
Many attempts have been made to apply machine learning techniques to constraint
satisfaction problems (CSPs). However, none of them have made use of the recent …
satisfaction problems (CSPs). However, none of them have made use of the recent …
Using the shapley value to analyze algorithm portfolios
Algorithms for NP-complete problems often have different strengths andweaknesses, and
thus algorithm portfolios often outperform individualalgorithms. It is surprisingly difficult to …
thus algorithm portfolios often outperform individualalgorithms. It is surprisingly difficult to …
Automated synthesis and deployment of cloud applications
R Di Cosmo, M Lienhardt, R Treinen… - Proceedings of the 29th …, 2014 - dl.acm.org
Complex networked applications are assembled by connecting software components
distributed across multiple machines. Building and deploying such systems is a challenging …
distributed across multiple machines. Building and deploying such systems is a challenging …
Embarrassingly parallel search in constraint programming
We introduce an Embarrassingly Parallel Search (EPS) method for solving constraint
problems in parallel, and we show that this method matches or even outperforms state-of-the …
problems in parallel, and we show that this method matches or even outperforms state-of-the …
Parallel constraint programming
Constraint programming (CP) is an efficient technique for solving combinatorial optimization
problems. In CP a problem is defined over variables that take values in domains and …
problems. In CP a problem is defined over variables that take values in domains and …
SUNNY-CP: a sequential CP portfolio solver
The Constraint Programming (CP) paradigm allows to model and solve Constraint
Satisfaction/Optimization Problems (CSPs/COPs). A CP Portfolio Solver is a particular …
Satisfaction/Optimization Problems (CSPs/COPs). A CP Portfolio Solver is a particular …
meSAT: multiple encodings of CSP to SAT
One approach for solving Constraint Satisfaction Problems (CSP)(and related Constraint
Optimization Problems (COP)) involving integer and Boolean variables is reduction to …
Optimization Problems (COP)) involving integer and Boolean variables is reduction to …