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Gradient method
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131	DownloadedtoRedistribution subject to SIAM license or copyright; see http://www.siam.org/journals/ojsa.php SIAM J. SCI. COMPUT. Vol. 36, No. 2, pp. C95–C118 c 2014 Society for Industrial an Add to Reading List Source URL: www.stat.berkeley.edu Language: English - Date: 2014-06-25 12:34:56 Numerical linear algebra Singular value decomposition Matrix theory Functional analysis Preconditioner Moore–Penrose pseudoinverse Kernel Conjugate gradient method Orthogonal matrix Algebra Linear algebra Mathematics
132	Optimization Algorithms, Implementations Add to Reading List Source URL: www.cse.ust.hk Language: English - Date: 2009-09-15 12:00:50 Operations research Gradient descent Gradient method Penalty method Conjugate gradient method Constraint optimization PROPT Multidisciplinary design optimization Numerical analysis Numerical linear algebra Mathematical optimization
133	CHAM Limited Pioneering CFD Software for Education & Industry CONCENTRATION DISTRIBUTION AND PRESSURE GRADIENT OF PARTICLE-WATER SLURRY FLOWS IN HORIZONTAL PIPES by Add to Reading List Source URL: www.cham.co.uk Language: English - Date: 2013-04-15 06:29:28 Physics Aerodynamics Piping Chemical engineering Continuum mechanics Reynolds number Viscosity No-slip condition Multiphase particle-in-cell method Fluid dynamics Fluid mechanics Dynamics
134	XIII International Conference on Input-Output Techniques University of Macerata, Italy August 21-25th, 2000. Vittorio Nicolardi (University of Bari – Faculty of Economics) Add to Reading List Source URL: www.iioa.org Language: English - Date: 2007-10-09 05:36:15 Matrix theory Numerical linear algebra Mathematical optimization Abstract algebra Matrix Conjugate gradient method Vector space Invertible matrix Lagrange multiplier Algebra Linear algebra Mathematics
135	Ch.7 Nonlinear optimization [Book, Chap. 5] To appreciate the vast difference between linear optimization and nonlinear optimization, consider the relation y = w0 + L Add to Reading List Source URL: www.ocgy.ubc.ca Language: English - Date: 2013-10-28 03:07:27 Applied mathematics Gradient descent Linear programming Quasi-Newton method Descent direction W0 Least squares Numerical analysis Mathematical optimization Operations research
136	Case Study 1: Estimating Click Probabilities SGD cont’d AdaGrad Machine Learning for Big Data CSE547/STAT548, University of Washington Add to Reading List Source URL: courses.cs.washington.edu Language: English - Date: 2015-04-02 14:33:26 Numerical linear algebra Convex optimization Mathematical optimization Subgradient method Gradient Mathematical analysis Numerical analysis Mathematics
137	15 Optimization Once you’ve gathered your data, selected a representation to work with, chosen a framework for function approximation, specified an error metric, and expressed your prior beliefs about the model, then Add to Reading List Source URL: fab.cba.mit.edu Language: English - Date: 2014-04-29 10:51:08 Nelder–Mead method Mathematical optimization Conjugate gradient method Gradient descent Simplex Numerical analysis Operations research Mathematics
138	Short Math Guide for LATEX Michael Downes American Mathematical Society Version[removed]), currently available at http://www.ams.org/tex/short-math-guide.html 1. Introduction This is a concise summary of recommen Add to Reading List Source URL: www.tex.uniyar.ac.ru Language: English - Date: 2012-04-06 06:48:45 Derivation of the conjugate gradient method Numerical linear algebra Classical orthogonal polynomials Numerical analysis Mathematics Physics
139	Learning with stochastic proximal gradient Lorenzo Rosasco ∗ DIBRIS, Universit`a di Genova Via Dodecaneso, [removed]Genova, Italy Add to Reading List Source URL: www.opt-ml.org Language: English - Date: 2014-12-10 11:28:36 Stochastic optimization Operations research Stochastic gradient descent Stochastic approximation Gradient descent Gradient method Subgradient method Convex optimization Numerical analysis Mathematical optimization Mathematical analysis
140	Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli Stanford University {jascha,benpoole,sganguli}@stanford.edu Add to Reading List Source URL: www.opt-ml.org Language: English - Date: 2014-12-13 10:45:07 Stochastic optimization Quasi-Newton method Gradient descent Stochastic gradient descent BFGS method Limited-memory BFGS Stochastic approximation Hessian matrix Perceptron Numerical analysis Mathematical optimization Mathematical analysis

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