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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 11111120 of 1277 papers

TitleStatusHype
The (Un)Scalability of Heuristic Approximators for NP-Hard Search ProblemsCode0
USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization ProblemsCode0
Learning General Policies from Small Examples Without SupervisionCode0
Structural Causal Models Reveal Confounder Bias in Linear Program ModellingCode0
LeadCache: Regret-Optimal Caching in NetworksCode0
Learning Heuristics over Large Graphs via Deep Reinforcement LearningCode0
Interferometric Neural NetworksCode0
Graph Neural Networks for the Offline Nanosatellite Task Scheduling ProblemCode0
Blackout DIFUSCOCode0
Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLMCode0
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