SOTAVerified

Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 111–120 of 1277 papers

TitleStatusHype
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
Learning-Based TSP-Solvers Tend to Be Overly Greedy—0
Regularized Langevin Dynamics for Combinatorial OptimizationCode0
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms—0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver—0
Making Sense Of Distributed Representations With Activation Spectroscopy—0
PSO and the Traveling Salesman Problem: An Intelligent Optimization Approach—0
Reinforcement Learning Constrained Beam Search for Parameter Optimization of Paper Drying Under Flexible Constraints—0
Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization—0
Show:102550
← PrevPage 12 of 128Next →

No leaderboard results yet.