Results 61 to 70 of about 43,906 (221)
The ADS general-purpose optimization program [PDF]
, 1984 The mathematical statement of the general nonlinear optimization problem is given as follows: find the vector of design variables, X, that will minimize f(X) subject to G sub J (x) + or - 0 j=1,m H sub K hk(X) = 0 k=1,l X Lower I approx less than X sub I
Vanderplaats, G. N.
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Toward Solution‐Time Advantage With Error‐Mitigated Quantum Annealing for Combinatorial Optimization
Advanced Physics Research, EarlyView.This paper presents a novel error mitigation technique to address the qubit errors that occur when solving combinatorial optimization problems with quantum annealing. The approach significantly speeds up the computation to reach the global optimum solution for a correlated 3D image segmentation model for material microstructures, demonstrating a ...
Yushuang Sam Yang +3 more
wiley +1 more source
An algorithm using quadratic interpolation for unconstrained optimization [PDF]
, 2015 Import 22/07/2015Práce se zabývá metodami pro minimalizaci funkce bez omezení. Klasické metody vyžadují znalost první derivace (gradientu), případně i druhé derivace (Hessiánu). Pokud je náročné tyto hodnoty vypočítat, pak se používají tzv.
Bednařík, Štěpán
core
Particle algorithms for optimization on binary spaces
, 2011 We discuss a unified approach to stochastic optimization of pseudo-Boolean objective functions based on particle methods, including the cross-entropy method and simulated annealing as special cases.
Schäfer, Christian
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Iterated Tabu Search for the Unconstrained Binary Quadratic Optimization Problem [PDF]
Informatica, 2006 Given a set of objects with profits (any, even negative, numbers) assigned not only to separate objects but also to pairs of them, the unconstrained binary quadratic optimization problem consists in finding a subset of objects for which the overall profit is maximized.
openaire +2 more sources
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Asian Journal of Control, EarlyView.Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source
Asian Journal of Control, EarlyView.Abstract The linear‐quadratic regulator (LQR) problem of optimal control of an uncertain discrete‐time linear system (DTLS) is revisited in this paper from the perspective of Tikhonov regularization. We show that an optimally chosen regularization parameter reduces, compared to the classical LQR, the values of a scalar error function, as well as the ...
Fernando Pazos, Amit Bhaya
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Greedy permanent magnet optimization
Nuclear Fusion, 2023 A number of scientific fields rely on placing permanent magnets in order to produce a desired magnetic field. We have shown in recent work that the placement process can be formulated as sparse regression.
Alan A. Kaptanoglu +2 more
doaj +1 more source
, 2010 Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio.
A Fernández +47 more
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Biotechnology and Bioengineering, EarlyView.ABSTRACT Adeno‐associated viral (AAV) vectors for gene therapy are becoming integral to modern medicine, providing therapeutic options for diseases once deemed incurable. Currently, viral vector purification is a critical bottleneck in the gene therapy industry, impacting product efficacy and safety as well as accessibility and cost to patients ...
Kelvin P. Idanwekhai +9 more
wiley +1 more source