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Perturbation Analysis of Optimization Problems
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It is shown here how the model derived recently in [Bouchut-Boyaval, M3AS (23) 2013] can be modified for flows on rugous topographies varying around an inclined plane.Abstract:
Basic notation.- Introduction.- Background material.- Optimality conditions.- Basic perturbation theory.- Second order analysis of the optimal value and optimal solutions.- Optimal Control.- References.read more
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Lectures on Stochastic Programming: Modeling and Theory
TL;DR: The authors dedicate this book to Julia, Benjamin, Daniel, Natan and Yael; to Tsonka, Konstatin and Marek; and to the Memory of Feliks, Maria, and Dentcho.
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Online Learning for Matrix Factorization and Sparse Coding
TL;DR: In this paper, a new online optimization algorithm based on stochastic approximations is proposed to solve the large-scale matrix factorization problem, which scales up gracefully to large data sets with millions of training samples.
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Choosing Multiple Parameters for Support Vector Machines
TL;DR: The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters.
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Online Learning for Matrix Factorization and Sparse Coding
TL;DR: A new online optimization algorithm is proposed, based on stochastic approximations, which scales up gracefully to large data sets with millions of training samples, and extends naturally to various matrix factorization formulations, making it suitable for a wide range of learning problems.
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Envelope Theorems for Arbitrary Choice Sets
Paul Milgrom,Ilya Segal +1 more
TL;DR: The standard envelope theorems apply to choice sets with convex and topological structure, providing sufficient conditions for the value function to be differentiable in a parameter and characterizing its derivative as mentioned in this paper.
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Stability and sensitivity-analysis for stochastic programming
TL;DR: Stability and sensitivity studies for stochastic programs have been motivated by the problem of incomplete information about the true probability measure through which the Stochastic program is formulated and in connection with the development and evaluation of algorithms as mentioned in this paper.