Convex Analysis and Minimization Algorithms I: Fundamentals by Jean-Baptiste Hiriart-Urruty, Claude Lemarechal

By Jean-Baptiste Hiriart-Urruty, Claude Lemarechal

Convex research should be regarded as a refinement of ordinary calculus, with equalities and approximations changed via inequalities. As such, it may well simply be built-in right into a graduate examine curriculum. Minimization algorithms, extra particularly these tailored to non-differentiable services, supply an instantaneous program of convex research to numerous fields relating to optimization and operations learn. those themes making up the name of the booklet, replicate the 2 origins of the authors, who belong respectively to the educational international and to that of purposes. half i will be used as an introductory textbook (as a foundation for classes, or for self-study); half II maintains this at the next technical point and is addressed extra to experts, amassing effects that up to now haven't seemed in books.

Show description

Read or Download Convex Analysis and Minimization Algorithms I: Fundamentals PDF

Best linear programming books

Combinatorial Data Analysis: Optimization by Dynamic Programming

Combinatorial information research (CDA) refers to a large classification of tools for the research of correct information units within which the association of a set of gadgets is really important. the focal point of this monograph is at the id of preparations, that are then additional constrained to the place the combinatorial seek is performed by means of a recursive optimization approach in line with the overall ideas of dynamic programming (DP).

Science Sifting: Tools for Innovation in Science and Technology

Technology Sifting is designed essentially as a textbook for college kids drawn to examine and as a normal reference e-book for current profession scientists. the purpose of this ebook is to aid budding scientists expand their capacities to entry and use info from different resources to the advantage of their study careers.

Extra resources for Convex Analysis and Minimization Algorithms I: Fundamentals

Example text

Then (/1 t 12)* = It + g . 1) PROOF. 2). For s E JR, (/1 t h)*(s) = + h(X2)J} = sUPXt+x2=AS(XI +X2) - II(xl) - h(x2)] = sUPXt,xJS(XI +X2) - II (XI) - h(x2)] SUPXt [SXI - II (x])] + SUPX2[SX2 - h(x2)] = and we recognize suPx {sx - infxt + x2 =x[f1 (x]) It(s) + Iz*(s) in this last expression. o The dual version of this result is that, if II and 12 are two closed convex functions finite at some common point, then (/1 + 12)* = It t g . 1, and their conjugates are II and fz respectively; hence (/t t Iz*)* = II + fz .

1, convexity of Ion I = [a, b] implies its upper semi-continuity at a and b. 1). We will therefore content ourselves with checking the convexity of a given function on an open interval. Then, checking convexity on the closure of that interval will reduce to a study of continuity, usually much easier. 1 Let 1 be continuous on an open interval I and possess an increasing right-derivative, or an increasing left-derivative, on I. Then 1 is convex on I. PROOF. Assume that 1 has an increasing right-derivative D+I.

U {+oo} for t f(xo + td) t t f(xo) =: q(t) to, in which case Xo + td i a. It follows 3 Continuity Properties 17 f(xo + td) = f(xo) + tq(t) --+ f(xo) + (xo - a)l =: f(a+) E lR U {+oo} . Then let t t to in the relation f(a) - f(xo) q(t) :,;; q(to) = for all t E ]0, toe a Xo - to obtain l= f(a+) - f(xo) Xo - a :,;; f(a) - f(xo) Xo - a , o hence f(a+) :,;; f(a). The prooffor b uses the same arguments. 3, and is nothing more than the slope-function [f(x) - f(xo)]/(x - xo). 3, to avoid the unpleasant division by x - Xo < O.

Download PDF sample

Rated 4.96 of 5 – based on 34 votes