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A versatile large-scale coherent Ising machine from microwave to visible and telecom wavelength bands. [PDF]
Zhang H, Li J, Zhu H.
europepmc +1 more source
ErB4 and NdB4 nanostructured powders are produced by mechanochemical synthesis. 5 h mechanical alloying and 4 M HCl acid leaching are used in the production. ErB4 and NdB4 powders exhibit maximum magnetization of 0.4726 emu g−1 accompanied with an antiferromagnetic‐to‐paramagnetic phase transition at about TN = 18 K and 0.132 emu g−1 with a maximum at ...
Burçak Boztemur +5 more
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Information-Theoretic Intrinsic Motivation for Reinforcement Learning in Combinatorial Routing. [PDF]
Xi R, Ni Y, Wu W.
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Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges. [PDF]
Kim S +5 more
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Interpretable and Constrained Machine Learning via Combinatorial Optimization
Pouya Shati
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Enhanced spinosad production in <i>Saccharopolyspora spinosa</i> by employing mannose as an extracellular carbon reservoir and optimizing acetyl-CoA supply pathway. [PDF]
Zhu Z +12 more
europepmc +1 more source
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Oberwolfach Reports, 2022
Combinatorial Optimization deals with optimization problems defined on combinatorial structures such as graphs and networks. Motivated by diverse practical problem setups, the topic has developed into a rich mathematical discipline with many connections to other fields of Mathematics (such as, e.g., Combinatorics, Convex Optimization and Geometry, and ...
Karen I. Aardal +3 more
openaire +2 more sources
Combinatorial Optimization deals with optimization problems defined on combinatorial structures such as graphs and networks. Motivated by diverse practical problem setups, the topic has developed into a rich mathematical discipline with many connections to other fields of Mathematics (such as, e.g., Combinatorics, Convex Optimization and Geometry, and ...
Karen I. Aardal +3 more
openaire +2 more sources
From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization
Neural Information Processing Systems, 2023Extensive experiments have gradually revealed the potential performance bottle-neck of modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks.
Yang Li +3 more
semanticscholar +1 more source
RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Knowledge Discovery and Data Mining, 2023Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation.
Federico Berto +9 more
semanticscholar +1 more source

