Journal ArticleDOI
Practical Methods of Optimization.
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This article is published in Mathematics of Computation.The article was published on 1989-10-01. It has received 9153 citations till now.read more
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QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials
Paolo Giannozzi,Stefano Baroni,Stefano Baroni,Nicola Bonini,Matteo Calandra,Roberto Car,Carlo Cavazzoni,Davide Ceresoli,Guido L. Chiarotti,Matteo Cococcioni,Ismaila Dabo,Andrea Dal Corso,Andrea Dal Corso,Stefano de Gironcoli,Stefano de Gironcoli,Stefano Fabris,Stefano Fabris,Guido Fratesi,Ralph Gebauer,Ralph Gebauer,Uwe Gerstmann,Christos Gougoussis,Anton Kokalj,Michele Lazzeri,Layla Martin-Samos,Nicola Marzari,Francesco Mauri,Riccardo Mazzarello,Stefano Paolini,Alfredo Pasquarello,Lorenzo Paulatto,Lorenzo Paulatto,Carlo Sbraccia,Sandro Scandolo,Sandro Scandolo,Gabriele Sclauzero,Gabriele Sclauzero,Ari P. Seitsonen,Alexander Smogunov,Paolo Umari,Renata M. Wentzcovitch +40 more
TL;DR: QUANTUM ESPRESSO as discussed by the authors is an integrated suite of computer codes for electronic-structure calculations and materials modeling, based on density functional theory, plane waves, and pseudopotentials (norm-conserving, ultrasoft, and projector-augmented wave).
Book
Numerical Optimization
Jorge Nocedal,Stephen J. Wright +1 more
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Journal ArticleDOI
Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.