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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 451–500 of 1277 papers

TitleStatusHype
Deep Momentum Uncertainty Hashing—0
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks—0
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology—0
Exact Spin Elimination in Ising Hamiltonians and Energy-Based Machine Learning—0
Deep memetic models for combinatorial optimization problems: application to the tool switching problem—0
Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms—0
Experiments with graph convolutional networks for solving the vertex p-center problem—0
Exploiting Problem Structure in Combinatorial Landscapes: A Case Study on Pure Mathematics Application—0
Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem—0
CoCo: Learning Strategies for Online Mixed-Integer Control—0
A Tutorial about Random Neural Networks in Supervised Learning—0
Exploring the Feature Space of TSP Instances Using Quality Diversity—0
Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm for Solving 0/1 Knapsack Problems—0
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry—0
Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning—0
Fair Disaster Containment via Graph-Cut Problems—0
Deep Learning of Graph Matching—0
Attention Round for Post-Training Quantization—0
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method—0
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization—0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training—0
Combinatorial optimization and reasoning with graph neural networks—0
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems—0
Faster Matchings via Learned Duals—0
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise—0
Faster width-dependent algorithm for mixed packing and covering LPs—0
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms—0
Fast Hyperparameter Tuning for Ising Machines—0
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem—0
Fast instance-specific algorithm configuration with graph neural network—0
Deep Generative Model for Mechanical System Configuration Design—0
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks—0
A Memetic Algorithm Based on Breakout Local Search for the Generalized Travelling Salesman Problem—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
GFPack++: Improving 2D Irregular Packing by Learning Gradient Field with Attention—0
Fewer Truncations Improve Language Modeling—0
Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem—0
Finding Support Examples for In-Context Learning—0
GNCGCP - Graduated NonConvexity and Graduated Concavity Procedure—0
First-Order Bayesian Regret Analysis of Thompson Sampling—0
Graph Neural Networks for Job Shop Scheduling Problems: A Survey—0
First-order regret bounds for combinatorial semi-bandits—0
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models—0
Fixed Priority Global Scheduling from a Deep Learning Perspective—0
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems—0
DeepDA: LSTM-based Deep Data Association Network for Multi-Targets Tracking in Clutter—0
A topological analysis of the space of recipes—0
DeepCO: Offline Combinatorial Optimization Framework Utilizing Deep Learning—0
Combinatorial Persistency Criteria for Multicut and Max-Cut—0
Deep Causal Learning: Representation, Discovery and Inference—0
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