Results 1 to 10 of about 4,882,239 (315)

Quantum-enhanced greedy combinatorial optimization solver. [PDF]

Sci Adv, 2023
Combinatorial optimization is a broadly attractive area for potential quantum advantage, but no quantum algorithm has yet made the leap. Noise in quantum hardware remains a challenge, and more sophisticated quantum-classical algorithms are required to ...
Dupont M, Evert B, Hodson MJ, Sundar B, Jeffrey S, Yamaguchi Y, Feng D, Maciejewski FB, Hadfield S, Alam MS, Wang Z, Grabbe S, Lott PA, Rieffel EG, Venturelli D, Reagor MJ.   +15 more
europepmc   +3 more sources

High-performance combinatorial optimization based on classical mechanics. [PDF]

Sci Adv, 2021
Massively parallel implementations of algorithms based on classical mechanics realize high-performance combinatorial optimization. Quickly obtaining optimal solutions of combinatorial optimization problems has tremendous value but is extremely difficult.
Goto H, Endo K, Suzuki M, Sakai Y, Kanao T, Hamakawa Y, Hidaka R, Yamasaki M, Tatsumura K.   +8 more
europepmc   +2 more sources

Combinatorial Optimization

Oberwolfach Reports
Combinatorial optimization deals with optimization problems defined on polyhedral constraints or discrete structures such as graphs and networks. In the past thirty years the topic has developed into a rich mathematical discipline with many connections to other fields of mathematics such as combinatorics, group theory, geometry of numbers, convex ...
Thomas Rothvoss, Laura Sanità, Robert Weismantel   +2 more
semanticscholar   +5 more sources

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics. [PDF]

Nat Commun, 2022
Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression.
Leppek K, Byeon GW, Kladwang W, Wayment-Steele HK, Kerr CH, Xu AF, Kim DS, Topkar VV, Choe C, Rothschild D, Tiu GC, Wellington-Oguri R, Fujii K, Sharma E, Watkins AM, Nicol JJ, Romano J, Tunguz B, Diaz F, Cai H, Guo P, Wu J, Meng F, Shi S, Participants E, Dormitzer PR, Solórzano A, Barna M, Das R.   +28 more
europepmc   +2 more sources

Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art

European Journal of Operational Research, 2021
In recent years, there has been a growing research interest in integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. This integration aims to lead meta-heuristics toward an efficient, effective, and
Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Patrick Meyer, Amir Mohammad Karimi-Mamaghan, El‐Ghazali Talbi   +4 more
openalex   +2 more sources

Nature-Inspired Metaheuristic Techniques for Combinatorial Optimization Problems: Overview and Recent Advances

Mathematics, 2021
Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been ...
Md Ashikur Rahman, Rajalingam Sokkalingam, Mahmod Othman, Kallol Biswas, Lazim Abdullah, Evizal Abdul Kadir   +5 more
doaj   +2 more sources

Probability-boosting technique for combinatorial optimization [PDF]

PeerJ Computer Science
In many combinatorial optimization problems we want a particular set of k out of n items with some certain properties (or constraints). These properties may involve the k items.
Sanpawat Kantabutra
doaj   +3 more sources

Combinatorial optimization by simulating adiabatic bifurcations in nonlinear Hamiltonian systems. [PDF]

Sci Adv, 2019
Nonlinear Hamiltonian systems search optimal solutions exploiting their adiabatic and chaotic evolutions. Combinatorial optimization problems are ubiquitous but difficult to solve.
Goto H, Tatsumura K, Dixon AR.
europepmc   +2 more sources

DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization [PDF]

Neural Information Processing Systems, 2023
Neural network-based Combinatorial Optimization (CO) methods have shown promising results in solving various NP-complete (NPC) problems without relying on hand-crafted domain knowledge.
Zhiqing Sun, Yiming Yang
semanticscholar   +1 more source

Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization [PDF]

Neural Information Processing Systems, 2023
Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts.
Fu Luo, Xi Lin, Fei Liu, Qingfu Zhang, Zhenkun Wang   +4 more
semanticscholar   +1 more source