Search Results for author:
Found 11 papers, 3 papers with code
Rethinking the Soft Conflict Pseudo Boolean Constraint on MaxSAT Local Search Solvers
In this paper, we propose to transfer the SPB constraint into the clause weighting system of the local search method, leading the algorithm to better solutions.
Incorporating Multi-armed Bandit with Local Search for MaxSAT
In this paper, we propose a local search algorithm for these problems, called BandHS, which applies two multi-armed bandits to guide the search directions when escaping local optima.
Hybrid Learning with New Value Function for the Maximum Common Subgraph Problem
Branch-and-Bound (BnB) is the basis of a class of efficient algorithms for MCS, consisting in successively selecting vertices to match and pruning when it is discovered that a solution better than the best solution found so far does not exist.
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman Problems
LKH-3 is a powerful extension of LKH that can solve many TSP variants.
BandMaxSAT: A Local Search MaxSAT Solver with Multi-armed Bandit
We address Partial MaxSAT (PMS) and Weighted PMS (WPMS), two practical generalizations of the MaxSAT problem, and propose a local search algorithm for these problems, called BandMaxSAT, that applies a multi-armed bandit model to guide the search direction.
Branching Strategy Selection Approach Based on Vivification Ratio
This approach uses the LRB branching strategy more to solve the instances with a very low vivification ratio.
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem
We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem.
Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem
Inspired by its success in deep learning, we apply the idea of SGD with batch selection of samples to a classic optimization problem in decision version.
A Learning based Branch and Bound for Maximum Common Subgraph Problems
Branch-and-bound (BnB) algorithms are widely used to solve combinatorial problems, and the performance crucially depends on its branching heuristic. In this work, we consider a typical problem of maximum common subgraph (MCS), and propose a branching heuristic inspired from reinforcement learning with a goal of reaching a tree leaf as early as possible to greatly reduce the search tree size. Extensive experiments show that our method is beneficial and outperforms current best BnB algorithm for the MCS.
An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem
Although Path-Relinking is an effective local search method for many combinatorial optimization problems, its application is not straightforward in solving the MAX-SAT, an optimization variant of the satisfiability problem (SAT) that has many real-world applications and has gained more and more attention in academy and industry.
Clause Vivification by Unit Propagation in CDCL SAT Solvers
Original and learnt clauses in Conflict-Driven Clause Learning (CDCL) SAT solvers often contain redundant literals.
