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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

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Showing 451–475 of 1277 papers

TitleStatusHype
Self-Improved Learning for Scalable Neural Combinatorial Optimization—0
Multi-Robot Connected Fermat Spiral CoverageCode0
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem—0
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization—0
Efficient Combinatorial Optimization via Heat DiffusionCode0
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network PruningCode0
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem—0
Deep Reinforcement Learning for Modelling Protein Complexes—0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm—0
Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instancesCode0
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems—0
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization—0
Box Facets and Cut Facets of Lifted Multicut Polytopes—0
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities—0
Towards Principled Task Grouping for Multi-Task Learning—0
PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization—0
RITFIS: Robust input testing framework for LLMs-based intelligent software—0
Reasoning Algorithmically in Graph Neural Networks—0
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
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training—0
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