Global Optimization of Statistical Functions with Simulated Annealing
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This implementation of simulated annealing was used in "Global Optimization of Statistical Functions with Simulated Annealing," Goffe, Ferrier and Rogers, Journal of Econometrics, vol.About:
This article is published in Journal of Econometrics.The article was published on 1994-01-01 and is currently open access. It has received 1665 citations till now. The article focuses on the topics: Simulated annealing & Adaptive simulated annealing.read more
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Local and global nucleon optical models from 1 keV to 200 MeV
Arjan J. Koning,J.P. Delaroche +1 more
TL;DR: In this article, the authors presented new phenomenological optical model potentials for neutrons and protons with incident energies from 1 keV up to 200 MeV, for (near-)spherical nuclides in the mass range 24⩽ A ⩽209 They are based on a smooth, unique functional form for the energy dependence of the potential depths, and on physically constrained geometry parameters.
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Simulated annealing: Practice versus theory
TL;DR: Using the author's Adaptive Simulated Annealing (ASA) code, some examples are given which demonstrate how SQ can be much faster than SA without sacrificing accuracy.
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Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization
TL;DR: This work investigates the use of kriging models as alternatives to traditional second-order polynomial response surfaces for constructing global approximations for use in a real aerospace engineering application, namely, the design of an aerospike nozzle.
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A Modified Principal Component Technique Based on the LASSO
TL;DR: The least absolute shrinkage and selection approach (LASSO) as mentioned in this paper is a technique for interpreting multiple regression equations, which is based on principal component analysis (PCA) in the context of multiple regression.
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Social interactions, local spillovers and unemployment
TL;DR: In this paper, a model that explicitly incorporates local interactions and allows agents to exchange information about job openings within their social networks is presented, where agents are more likely to be employed if their social contacts are also employed.
References
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Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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Numerical Recipes, The Art of Scientific Computing
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Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
TL;DR: In this paper, Schnabel proposed a modular system of algorithms for unconstrained minimization and nonlinear equations, based on Newton's method for solving one equation in one unknown convergence of sequences of real numbers.
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Numerical methods for unconstrained optimization and nonlinear equations
TL;DR: Newton's Method for Nonlinear Equations and Unconstrained Minimization and methods for solving nonlinear least-squares problems with Special Structure.