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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 221230 of 1277 papers

TitleStatusHype
Annealed Training for Combinatorial Optimization on Graphs0
Chases and Escapes, and Optimization Problems0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem0
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions0
Clustering Binary Data by Application of Combinatorial Optimization Heuristics0
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology0
Annealed Mean Field Descent Is Highly Effective for Quadratic Unconstrained Binary Optimization0
The Curious Case of Class Accuracy Imbalance in LLMs: Post-hoc Debiasing via Nonlinear Integer Programming0
CoCo: Learning Strategies for Online Mixed-Integer Control0
A Fitness Landscape View on the Tuning of an Asynchronous Master-Worker EA for Nuclear Reactor Design0
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