Results 11 to 20 of about 419,297 (312)
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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On testing global optimization algorithms for space trajectory design [PDF]
, 2008 In this paper we discuss the procedures to test a global search algorithm applied to a space trajectory design problem. Then, we present some performance indexes that can be used to evaluate the effectiveness of global optimization algorithms.
Marco Locatelli +10 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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A hybrid multiagent approach for global trajectory optimization [PDF]
, 2008 In this paper we consider a global optimization method for space trajectory design problems. The method, which actually aims at finding not only the global minimizer but a whole set of low-lying local minimizers(corresponding to a set of different design
Vasile, M. +4 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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A global optimization approach for the linear two-level program [PDF]
, 1993 Linear two-level programming deals with optimization problems in which the constraint region is implicity determined by another optimization problem. Mathematical programs of this type arise in connection with policy problems to which the Stackelberg ...
Tuy, Hoang, +2 more
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Global descent methods for unconstrained global optimization [PDF]
, 2010 Global descent method, Global optimization, Local search, Modified function approach, Non-convex optimization,
D. Li +7 more
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