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Date: 2016-06-04 09:49:43 Mathematical optimization Numerical analysis Mathematical analysis Operations research Linear programming Convex optimization Convex analysis Ellipsoid method Feasible region Convex function Linear inequality Candidate solution		CS168: The Modern Algorithmic Toolbox Lecture #18: Linear and Convex Programming, with Applications to Sparse Recovery Tim Roughgarden & Gregory Valiant∗ May 25, 2016 Add to Reading List Source URL: theory.stanford.edu Download Document from Source Website File Size: 305,83 KB Share Document on Facebook

	Hausdorﬀ Center for Mathematics, Summer School (May 9–13, 2016) Problems for “Discrete Convex Analysis” (by Kazuo Murota) Problem 1. Prove that a function f : Z2 → R defined by f (x1 , x2 ) = φ(x1 − x2 ) is DocID: 1vjVY - View Document
	Smoothed Analysis of Partitioning Algorithms for Euclidean Functionals∗ Markus Bl¨aser1 Bodo Manthey2 DocID: 1rtnz - View Document
	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 DocID: 1rrJ2 - View Document
	CS168: The Modern Algorithmic Toolbox Lecture #18: Linear and Convex Programming, with Applications to Sparse Recovery Tim Roughgarden & Gregory Valiant∗ May 25, 2016 DocID: 1rjsj - View Document
	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 DocID: 1rfrF - View Document