Results 11 to 20 of about 164,723 (287)
A Global Optimizer for Nanoclusters [PDF]
Frontiers in Chemistry, 2019 We have developed an algorithm to automatically build the global minimum and other low-energy minima of nanoclusters. This method is implemented in PyAR (https://github.com/anooplab/pyar) program. The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries ...
Maya Khatun +2 more
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Global Optimization Requires Global Information [PDF]
Journal of Optimization Theory and Applications, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stephens, C. P., Baritompa, W.
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Energy Reports, 2022 Desalination and atmospheric water harvesting technologies are highly desirable to produce freshwater for daily life activities and alleviate the global water crisis.
Trevor Hocksun Kwan +3 more
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Global Optimization on an Interval [PDF]
Journal of Optimization Theory and Applications, 2016 The problem is that of finding all global maxima of a continuously differentiable real valued objective function \(F(t)\) in a closed interval \([0, T]\) (this includes possible maxima on the boundary). For this, two auxiliary functions or \textit{adjoint variables} \(x(t),\) \(y(t)\) are introduced, solution of the initial value problems \[ x'(s ...
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Learning to be Global Optimizer
CoRR, 2020 The advancement of artificial intelligence has cast a new light on the development of optimization algorithm. This paper proposes to learn a two-phase (including a minimization phase and an escaping phase) global optimization algorithm for smooth non-convex functions. For the minimization phase, a model-driven deep learning method is developed to learn
Haotian Zhang, Jianyong Sun, Zongben Xu
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Distributed global optimization (DGO) [PDF]
Proceedings of International Conference on Neural Networks (ICNN'96), 2002 A new technique of global optimization and its applications in particular to neural networks are presented. The algorithm is also compared to other global optimization algorithms such as Gradient descent (GD), Monte Carlo (MC), Genetic Algorithm (GA) and other commercial packages.
Homayoun Valafar +2 more
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Global Optimality in Low-Rank Matrix Optimization [PDF]
IEEE Transactions on Signal Processing, 2017 This paper considers the minimization of a general objective function $f(X)$ over the set of rectangular $n\times m$ matrices that have rank at most $r$. To reduce the computational burden, we factorize the variable $X$ into a product of two smaller matrices and optimize over these two matrices instead of $X$. Despite the resulting nonconvexity, recent
Zhihui Zhu +3 more
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Global optimization for low-dimensional switching linear regression and bounded-error estimation [PDF]
, 2017 The paper provides global optimization algorithms for two particularly difficult nonconvex problems raised by hybrid system identification: switching linear regression and bounded-error estimation.
Lauer, Fabien
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Cooperative Output Regulation By Q-learning For Discrete Multi-agent Systems In Finite-time
Journal of Applied Science and Engineering, 2022 This article studies the output regulation of discrete-time multi-agent systems with an unknown model by a finite-time optimal control algorithm based on Q-learning that uses the method of the linear quadratic regulator (LQR).
Wenjun Wei, Jingyuan Tang
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Global Optimality Conditions for Nonlinear Programming Problems with Linear Equality Constraints
Journal of Applied Mathematics, 2014 Some necessary global optimality conditions and sufficient global optimality conditions for nonconvex minimization problems with a quadratic inequality constraint and a linear equality constraint are derived.
Guoquan Li, Yan Wang
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