<--- Back to Details
First PageDocument Content
Convex analysis / Convex geometry / Discrete geometry / Topological vector spaces / Mathematical optimization / Convex hull / Convex set / Convex function / Krein–Milman theorem / Mathematics / Mathematical analysis / Geometry
Date: 2012-08-27 07:55:36
Convex analysis
Convex geometry
Discrete geometry
Topological vector spaces
Mathematical optimization
Convex hull
Convex set
Convex function
Krein–Milman theorem
Mathematics
Mathematical analysis
Geometry

∗ Convex sets and their integral representations ∗

Add to Reading List

Source URL: www.math.ku.dk

Download Document from Source Website

File Size: 414,54 KB

Share Document on Facebook

Similar Documents

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

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

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

DocID: 1rtnz - View Document

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

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

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

DocID: 1rjsj - View Document

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

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