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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 121130 of 1277 papers

TitleStatusHype
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial OptimizationCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
MMSR: Symbolic Regression is a Multi-Modal Information Fusion TaskCode1
Monte Carlo Policy Gradient Method for Binary OptimizationCode1
ASP: Learn a Universal Neural Solver!Code1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Attention, Learn to Solve Routing Problems!Code1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Belief Propagation Neural NetworksCode1
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