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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 301–325 of 1277 papers

TitleStatusHype
An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm—0
An efficient algorithm for learning with semi-bandit feedback—0
Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints—0
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks—0
A Bayesian approach for prompt optimization in pre-trained language models—0
An Efficient Algorithm for Cooperative Semi-Bandits—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction—0
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling—0
A Weighted Common Subgraph Matching Algorithm—0
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing—0
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles—0
Automatic Rank Selection for High-Speed Convolutional Neural Network—0
An Approximation Algorithm for Risk-averse Submodular Optimization—0
Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits—0
Digging Deeper: Operator Analysis for Optimizing Nonlinearity of Boolean Functions—0
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property—0
Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations—0
Differentiable Scaffolding Tree for Molecular Optimization—0
Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection—0
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy—0
Differentiable Scaffolding Tree for Molecule Optimization—0
Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design—0
A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based Approaches for E-GTSP—0
Analysis of Quality Diversity Algorithms for the Knapsack Problem—0
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