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 321–330 of 1277 papers

TitleStatusHype
Convergence Acceleration of Markov Chain Monte Carlo-based Gradient Descent by Deep Unfolding—0
Large Scale Constrained Clustering With Reinforcement Learning—0
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution ShiftsCode0
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization—0
Assortment Planning with Sponsored Products—0
Moco: A Learnable Meta Optimizer for Combinatorial OptimizationCode1
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training—0
Effective anytime algorithm for multiobjective combinatorial optimization problems—0
torchmSAT: A GPU-Accelerated Approximation To The Maximum Satisfiability Problem—0
Accelerating Matroid Optimization through Fast Imprecise Oracles—0
Show:102550
← PrevPage 33 of 128Next →

No leaderboard results yet.