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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 941950 of 1277 papers

TitleStatusHype
Modeling Local Search Metaheuristics Using Markov Decision Processes0
Eliciting and Distinguishing Between Weak and Incomplete Preferences: Theory, Experiment and Computation0
MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization0
Modular Multi Target Tracking Using LSTM Networks0
MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems0
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design0
Monotone comparative statics for submodular functions, with an application to aggregated deferred acceptance0
MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search0
MRF Optimization with Separable Convex Prior on Partially Ordered Labels0
mRNA Codon Optimization on Quantum Comptuers0
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