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 126–150 of 1277 papers

TitleStatusHype
Decision-Focused Learning: Through the Lens of Learning to RankCode1
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming SeedingCode1
Active Learning Meets Optimized Item SelectionCode1
One model Packs Thousands of Items with Recurrent Conditional Query LearningCode1
Symbolic Regression via Neural-Guided Genetic Programming Population SeedingCode1
OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit SynthesisCode1
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender SystemCode1
Hybrid Pointer Networks for Traveling Salesman Problems OptimizationCode1
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
Rationales for Sequential PredictionsCode1
RAMA: A Rapid Multicut Algorithm on GPUCode1
Learning a Large Neighborhood Search Algorithm for Mixed Integer ProgramsCode1
Maximum Entropy Weighted Independent Set Pooling for Graph Neural NetworksCode1
Combinatorial Optimization with Physics-Inspired Graph Neural NetworksCode1
Learning Primal Heuristics for Mixed Integer ProgramsCode1
Matrix Encoding Networks for Neural Combinatorial OptimizationCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Noisy intermediate-scale quantum algorithm for semidefinite programmingCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
Self-Supervision is All You Need for Solving Rubik's CubeCode1
Implicit MLE: Backpropagating Through Discrete Exponential Family DistributionsCode1
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Solve routing problems with a residual edge-graph attention neural networkCode1
Show:102550
← PrevPage 6 of 52Next →

No leaderboard results yet.