Titlebook: Convex Analysis and Minimization Algorithms II; Advanced Theory and Jean-Baptiste Hiriart-Urruty,Claude Lemaréchal Book 1993 Springer-Verl

挑剔小責(zé)

Voices of the Soviet Space ProgramBasic concepts of numerical optimization (Chap. 11); descent principles for nonsmooth minimization (Chaps. IX and XIII); definition and elementary properties of approximate subdifferentials (Chap. XI); and to a lesser extent: minirnality conditions for simple minimization problems, and elements of duality theory (Chaps. VII and XII).

MERIT

febrile

Conjugacy in Convex Analysis,Definitions, properties and operations concerning convex sets (Chap. III) and convex functions (Chap. IV); definition of sublinear functions and associated convex sets (Chap. V); definitions of the subdifferential of a finite convex function (§VI.l). Onedimensional conjugacy (§I.6) can be helpful but is not necessary.

掃興

Approximate Subdifferentials of Convex Functions,Sublinear functions and associated convex sets (Chap. V); characterization of the subdifferential via the conjugacy correspondence (§X.l.4); calculus mIes on conjugate functions (§X.2); and also: behaviour at infinity of one-dimensional convex functions (§I.2.3).

ANT

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HPA533

Triglyceride

Acceleration of the Cutting-Plane Algorithm: Primal Forms of Bundle Methods,le convex minimization problems) will suffice for a superficial reading. However, practically all the book is useful for a complete understanding of every detail; in particular: Chap. 11 (basic principies for minimization algorithms), Chap. IV (properties ofperspective-functions), Chap. IX (bundling

Pituitary-Gland

Book 1993ain everything from rigorous proofs to tables of numerical calculations.... one of the strong features of these books...that they are designed not for the expert, but for those who whish to learn the subject matter starting from little or no background...there are numerous examples, and counter-exam

銼屑

Acceleration of the Cutting-Plane Algorithm: Primal Forms of Bundle Methods, mechanism for descent algorithms), Chap. X (conjugacy, smoothness of the conjugate), Chap. XI (approximate subdifferentials, infimal convolution), Chap. XII (classical algorithms for convex minimization, duality schemes in the convex case), Chap. XIV (general principles of dual bundle methods).