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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 551560 of 1277 papers

TitleStatusHype
A Simulated Annealing-Based Multiobjective Optimization Algorithm for Minimum Weight Minimum Connected Dominating Set Problem0
Actively Learning Combinatorial Optimization Using a Membership Oracle0
Artificial Catalytic Reactions in 2D for Combinatorial Optimization0
Cost-aware Feature Selection for IoT Device Classification0
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows0
A2Perf: Real-World Autonomous Agents Benchmark0
Implicitly Intersecting Weighted Automata using Dual Decomposition0
Improved Approximation Algorithms for Low-Rank Problems Using Semidefinite Optimization0
Cortical Processing with Thermodynamic-RAM0
COPS: Controlled Pruning Before Training Starts0
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