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 1001–1010 of 1277 papers

TitleStatusHype
On combinatorial optimization for dominating sets (literature survey, new models)—0
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models—0
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds—0
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK—0
Online combinatorial optimization with stochastic decision sets and adversarial losses—0
Online learning for min-max discrete problems—0
ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers—0
On permutation symmetries in Bayesian neural network posteriors: a variational perspective—0
On Size Generalization in Graph Neural Networks—0
From Local Structures to Size Generalization in Graph Neural Networks—0
Show:102550
← PrevPage 101 of 128Next →

No leaderboard results yet.