Convex Analysis is an emerging calculus of inequalities while Convex Optimization is its application. Analysis is the domain of a mathematician while Optimization belongs to the engineer. In layman's terms, the mathematical science of Optimization is a study of how to make good choices when confronted with conflicting requirements and demands. The qualifier Convex means: when an optimal solution is found, then it is guaranteed to be a best solution; there is no better choice. As any Convex Optimization problem has geometric interpretation, this book is about convex geometry (with particular attention to distance geometry) and nonconvex, combinatorial, and geometrical problems that can be relaxed or transformed into convex problems. A virtual flood of new applications follows by epiphany that many problems, presumed nonconvex, can be so transformed. This is a BLACK a WHITE paperback. A hardcover with full color interior, as originally conceived, is available at lulu.com/spotlight/dattorroJournal of Approximation Theory, 124(2):194a218, October 2003. http://www. optimization-online.org/DB FILE/2003/06/669.pdf Mike Brookes. Matrix reference manual: Matrix calculus, 2002. http://www.ee.ic.ac.uk/hp/staff/dmb/matrix/intro. html J. P. Brooks, J. H. DulAi, and E. L. ... Michael Saunders, and Yinyu Ye. spaseloc: An adaptive subproblem algorithm for scalable wireless sensor network localization.
|Title||:||Convex Optimization Euclidean Distance Geometry 2e|
|Publisher||:||Lulu.com - 2015-07-21|