Convex Analysis and Nonlinear Optimization: Theory and by Jonathan M. Borwein

By Jonathan M. Borwein

Optimization is a wealthy and thriving mathematical self-discipline, and the underlying thought of present computational optimization innovations grows ever extra refined. This e-book goals to supply a concise, available account of convex research and its purposes and extensions, for a huge viewers. each one part concludes with a frequently wide set of not obligatory workouts. This re-creation provides fabric on semismooth optimization, in addition to a number of new proofs.

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23. {$) n 8f(y) = 0. Deduce that f has at most one minimizer. 24. (Minimizers of essentially smooth functions) Prove that any minimizer of an essentially smooth function f must lie in core (dom f). 25. ** (Convex matrix functions) Consider a matrix C inS+.. 1 Subgradients and Convex Functions 41 (a) For matrices X in S++ and D in gn, use a power series expansion to prove (b) Deduce XES++ f-> tr(CX- 1 ) is convex. (c) Prove similarly the function X E gn f-> tr (CX 2 ) and the function XES+ f-> -tr (CX 112 ) are convex.

The result is clear form= 0. Suppose then that the result holds in any Euclidean space and for any set of m - 1 elements and any element c. Define a 0 = -c. 1, ... iai. o = 0 and without loss of generality Am > 0. Define a subspace of E by Y = {y I (am, y) = 0}, so by assumption the system (ai,y) ~ 0 fori= 1,2, ... ,m -1, (c,y) > 0, y E Y, or equivalently (Pyai,y) ~ 0 fori= 1,2, ... ,m -1, (Pyc,y) > 0, y E Y, has no solution. L2, ... L. 10) to obtain a solution. Liai I0 '5:. 11) 1 can be separated from C by a hyperplane.

We consider the effect of applying a unit "potential difference" between the vertices a and {3. Let Vo = V \ {a, {3}, and for "potentials" x in R Vo we define the "power" p : R Vo -+ R by where we set Xa = 0 and X[j = 1. (a) Prove the power function p has compact level sets. (b) Deduce the existence of a solution to the following equations (describing "conservation of current"): I: ijEE j : Xi - X j Tij Xa X[j = 0 fori in Vo = = 0 1. 1 Optimality Conditions 21 (c) Prove the power function p is strictly convex.

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