We present a bandit algorithm, SAO (Stochastic and Adversarial Optimal), whose regret is, essentially, optimal both for adversarial rewards and for stochastic rewards. Specifical...
In this work we present the Partner Units Problem as a novel challenge for optimization methods. It captures a certain type of configuration problem that frequently occurs in indu...
Markus Aschinger, Conrad Drescher, Gerhard Friedri...
We consider the problem of finding generalized plans for situations where the number of objects may be unknown and unbounded during planning. The input is a domain specification...
Siddharth Srivastava, Neil Immerman, Shlomo Zilber...
The rectangular assignment problem is a generalization of the linear assignment problem (LAP): one wants to assign a number of persons to a smaller number of jobs, minimizing the ...
This work presents the concept of Continuous Search (CS), which objective is to allow any user to eventually get their constraint solver achieving a top performance on their proble...
This work presents the design and implementation of a massively parallel 3-SAT solver, specifically targeting random problem instances. Our approach is deterministic and features ...
Quirin Meyer, Fabian Schonfeld, Marc Stamminger, R...
Abstract. We study the situation in which, having solved a linear program with an interiorpoint method, we are presented with a new problem instance whose data is slightly perturbe...
In this technical report we briefly describe the instances submitted to the 2006 and 2007 MaxSAT Evaluations. First, we introduce the instances that can be directly encoded as Max...
Federico Heras, Javier Larrosa, Simon de Givry, Th...
It has been widely observed that there is no single "dominant" SAT solver; instead, different solvers perform best on different instances. Rather than following the trad...
Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton...
5 In the probabilistic traveling salesman problem (PTSP), customers require a visit with a given probability, and the best solution is the tour through all customers with the lowe...