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 151–175 of 1277 papers

TitleStatusHype
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
Equivariant quantum circuits for learning on weighted graphsCode1
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsCode1
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization AlgorithmsCode1
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot TeamsCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Belief Propagation Neural NetworksCode1
Combinatorial Optimization with Physics-Inspired Graph Neural NetworksCode1
Attention, Learn to Solve Routing Problems!Code1
HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMsCode1
Implicit MLE: Backpropagating Through Discrete Exponential Family DistributionsCode1
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-CutCode1
Instance-wise algorithm configuration with graph neural networksCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
L0Learn: A Scalable Package for Sparse Learning using L0 RegularizationCode1
Large Language Models as Evolutionary OptimizersCode1
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop SchedulingCode1
Learning Large Neighborhood Search for Vehicle Routing in Airport Ground HandlingCode1
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural NetworksCode1
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial SolversCode1
Show:102550
← PrevPage 7 of 52Next →

No leaderboard results yet.