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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 801810 of 1277 papers

TitleStatusHype
TSS GAZ PTP: Towards Improving Gumbel AlphaZero with Two-stage Self-play for Multi-constrained Electric Vehicle Routing Problems0
Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning0
Two-Dimensional Phase Unwrapping via Balanced Spanning Forests0
Two-Stage Learning For the Flexible Job Shop Scheduling Problem0
UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning0
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization0
UniCO: Towards a Unified Model for Combinatorial Optimization Problems0
Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set0
Unsupervised Learning for Quadratic Assignment0
Unveiling the Lexical Sensitivity of LLMs: Combinatorial Optimization for Prompt Enhancement0
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