Search Results for author:
Found 13 papers, 3 papers with code
Local Search for Integer Quadratic Programming
Local search is a powerful method for solving hard problems, but the research on local search algorithms for IQP solving is still on its early stage.
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach
Compared to previous heuristic ALS methods, the guarantee of optimality achieves a more controllable trade-off between circuit complexity and accuracy.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis
Though numerous solvers have been proposed for the MaxSAT problem, and the benchmark environment such as MaxSAT Evaluations provides a platform for the comparison of the state-of-the-art solvers, existing assessments were usually evaluated based on the quality, e. g., fitness, of the best-found solutions obtained within a given running time budget.
AutoSAT: Automatically Optimize SAT Solvers via Large Language Models
Conflict-Driven Clause Learning (CDCL) is the mainstream framework for solving the Satisfiability problem (SAT), and CDCL solvers typically rely on various heuristics, which have a significant impact on their performance.
New Characterizations and Efficient Local Search for General Integer Linear Programming
We propose a new local search framework that switches between three modes, namely Search, Improve, and Restore modes.
LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph Generation
Besides, representation-level unbiased strategies endow LANDMARK the advantage of compatibility with other methods.
Incremental Satisfiability Modulo Theory for Verification of Deep Neural Networks
Moreover, based on the framework, we propose the multi-objective DNN repair problem and give an algorithm based on our incremental SMT solving algorithm.
DeepSAT: An EDA-Driven Learning Framework for SAT
Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling.
Can Graph Neural Networks Learn to Solve MaxSAT Problem?
With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning.
NLocalSAT: Boosting Local Search with Solution Prediction
However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers.
Combining Reinforcement Learning and Configuration Checking for Maximum k-plex Problem
Based on the BLP and DTCC, we develop a local search algorithm named BDCC and improve it by a hyperheuristic strategy.
Local Search for Minimum Weight Dominating Set with Two-Level Configuration Checking and Frequency Based Scoring Function
This paper proposes a new local search algorithm for the MWDS problem, which is based on two new ideas.
NuMVC: An Efficient Local Search Algorithm for Minimum Vertex Cover
These two strategies are used in designing a new MVC local search algorithm, which is referred to as NuMVC.
