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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

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Showing 926–950 of 1277 papers

TitleStatusHype
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence—0
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization—0
Memcomputing: Leveraging memory and physics to compute efficiently—0
Message Passing and Combinatorial Optimization—0
Message Passing Variational Autoregressive Network for Solving Intractable Ising Models—0
Metaheuristics and Large Language Models Join Forces: Towards an Integrated Optimization Approach—0
Métodos de Otimização Combinatória Aplicados ao Problema de Compressão MultiFrases—0
MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms—0
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs—0
MineReduce: an approach based on data mining for problem size reduction—0
Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss—0
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation—0
Minimum Weight Perfect Matching via Blossom Belief Propagation—0
Mixed Uncertainty Sets for Robust Combinatorial Optimization—0
ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data—0
Modeling Local Search Metaheuristics Using Markov Decision Processes—0
Eliciting and Distinguishing Between Weak and Incomplete Preferences: Theory, Experiment and Computation—0
MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization—0
Modular Multi Target Tracking Using LSTM Networks—0
MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems—0
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design—0
Monotone comparative statics for submodular functions, with an application to aggregated deferred acceptance—0
MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search—0
MRF Optimization with Separable Convex Prior on Partially Ordered Labels—0
mRNA Codon Optimization on Quantum Comptuers—0
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