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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

Showing 476500 of 1277 papers

TitleStatusHype
Neural Airport Ground HandlingCode1
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural NetworksCode1
Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits0
ASP: Learn a Universal Neural Solver!Code1
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization0
Finding Support Examples for In-Context Learning0
Learning Large Neighborhood Search for Vehicle Routing in Airport Ground HandlingCode1
Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture0
Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design0
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
Graph Adversarial Immunization for Certifiable RobustnessCode0
Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment0
A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms0
Digging Deeper: Operator Analysis for Optimizing Nonlinearity of Boolean Functions0
Nonlinear Random Matrices and Applications to the Sum of Squares Hierarchy0
Approximately Optimal Core Shapes for Tensor Decompositions0
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet ControlCode0
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems0
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set0
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning0
Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin SplittingCode0
DeciLS-PBO: an Effective Local Search Method for Pseudo-Boolean OptimizationCode0
Learning To Dive In Branch And Bound0
Two-Stage Learning For the Flexible Job Shop Scheduling Problem0
Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex NetworksCode0
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