Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization Canyi Lu1 , Jiashi Feng1 , Yudong Chen2 , Wei Liu3 , Zhouchen Lin4,5,∗, Shuicheng Yan6,1 1 2

Source URL: www.cis.pku.edu.cn

Many data analysis applications require the solution of optimization problems involving a sum of large number of functions. We consider the problem of minimizing a sum of n functions over a convex constraint set. Algorit

Source URL: mmds-data.org

The International Workshop on Nonlinear Analysis and Convex Analysis Research Institute for Mathematical Sciences, Kyoto University Oiwake-town Kita-Shirakawa, Sakyou-ward, Kyoto-city, JAPAN This Workshop is qualified b

Source URL: www.kurims.kyoto-u.ac.jp

Smoothed Analysis of Partitioning Algorithms for Euclidean Functionals∗ Markus Bl¨aser1 Bodo Manthey2

Source URL: www-cc.cs.uni-saarland.de

• Mathematical analysis
• Mathematics
• Analysis
• Generalized functions
• Smooth functions
• Operations research
• Travelling salesman problem
• Distribution
• Limit of a function
• Approximation algorithm
• Convex function
• Euclidean algorithm

REVIEW SHEET FOR FINAL: ADVANCED MATH 195, SECTION 59 (VIPUL NAIK) To maximize efficiency, please bring a copy (print or readable electronic) of this review sheet to all review sessions. 1. Directional derivatives and gr

Source URL: files.vipulnaik.com

• Mathematical analysis
• Mathematics
• Calculus
• Multivariable calculus
• Differential calculus
• Convex analysis
• Mathematical optimization
• Derivative
• Lagrange multiplier
• Quasiconvex function
• Hessian matrix
• Gradient

Many data analysis applications require the solution of optimization problems involving a sum of large number of functions. We consider the problem of minimizing a sum of n functions over a convex constraint set. Algorit

Source URL: mmds-data.org

CS168: The Modern Algorithmic Toolbox Lecture #18: Linear and Convex Programming, with Applications to Sparse Recovery Tim Roughgarden & Gregory Valiant∗ May 25, 2016

Source URL: theory.stanford.edu

• Mathematical optimization
• Numerical analysis
• Mathematical analysis
• Operations research
• Linear programming
• Convex optimization
• Convex analysis
• Ellipsoid method
• Feasible region
• Convex function
• Linear inequality
• Candidate solution

Duality for increasing convex functionals with countably many marginal constraints D. Bartl∗ P. Cheridito†

Source URL: www.mat.univie.ac.at

• Mathematical analysis
• Analysis
• Measure theory
• Generalized functions
• Smooth functions
• Transportation theory
• Distribution
• Linear temporal logic

ONE-ONE FUNCTIONS AND INVERSES MATH 152, SECTION 55 (VIPUL NAIK) Corresponding material in the book: Section 7.1. What students should definitely get: The definition of one-to-one function, the computational and checking

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• Mathematical analysis
• Mathematics
• Analysis
• Functions and mappings
• Inverse function
• Function
• Convex function
• Injective function
• Continuous function
• Derivative
• Bijection
• Limit of a function

ETH-Japan Workshop Engelberg Advertising Randomized derivative-free optimization Sebastian Stich ETH Z¨

Source URL: sstich.ch

• Mathematical optimization
• Mathematical analysis
• Analysis
• Convex analysis
• Operations research
• Convex function
• Derivative