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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 351–375 of 1277 papers

TitleStatusHype
D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysis—0
WardropNet: Traffic Flow Predictions via Equilibrium-Augmented LearningCode0
Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints—0
Synthesizing Interpretable Control Policies through Large Language Model Guided SearchCode0
Rethinking Selection in Generational Genetic Algorithms to Solve Combinatorial Optimization Problems: An Upper Bound-based Parent Selection Strategy for Recombination—0
CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs—0
MG-Net: Learn to Customize QAOA with Circuit Depth AwarenessCode0
A 2-approximation algorithm for the softwired parsimony problem on binary, tree-child phylogenetic networks—0
Multi-objective Evolution of Heuristic Using Large Language Model—0
Quantum evolutionary algorithm for TSP combinatorial optimisation problem—0
Extended Deep Submodular Functions—0
Offline Reinforcement Learning for Learning to Dispatch for Job Shop SchedulingCode0
Machine Learning and Constraint Programming for Efficient Healthcare Scheduling—0
Diversity-Driven View Subset Selection for Indoor Novel View SynthesisCode0
Deep Generative Model for Mechanical System Configuration Design—0
Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning—0
Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based SamplingCode0
A GREAT Architecture for Edge-Based Graph Problems Like TSPCode0
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization—0
An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing—0
GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning—0
Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning—0
An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut ProblemCode0
Decision-Focused Learning to Predict Action Costs for PlanningCode0
Robust Estimation of Regression Models with Potentially Endogenous Outliers via a Modern Optimization Lens—0
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