New PDF release: Numerical Optimization (2nd Edition) (Springer Series in

By Stephen J. Wright, Jorge Nocedal

ISBN-10: 0387400656

ISBN-13: 9780387400655

Numerical Optimization provides a entire and up to date description of the best tools in non-stop optimization. It responds to the starting to be curiosity in optimization in engineering, technology, and enterprise by way of targeting the tools which are most suitable to functional problems.

For this re-creation the booklet has been completely up to date all through. There are new chapters on nonlinear inside equipment and derivative-free equipment for optimization, either one of that are used commonly in perform and the point of interest of a lot present examine. end result of the emphasis on sensible tools, in addition to the vast illustrations and workouts, the ebook is available to a large viewers. it may be used as a graduate textual content in engineering, operations study, arithmetic, machine technology, and enterprise. It additionally serves as a guide for researchers and practitioners within the box. The authors have strived to provide a textual content that's friendly to learn, informative, and rigorous - person who finds either the attractive nature of the self-discipline and its functional part.

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Additional resources for Numerical Optimization (2nd Edition) (Springer Series in Operations Research and Financial Engineering)

Example text

7 Poorly scaled and well scaled problems, and performance of the steepest descent direction. optimal values of z will be within about an order of magnitude of 1, making the solution more balanced. This kind of scaling of the variables is known as diagonal scaling. Scaling is performed (sometimes unintentionally) when the units used to represent variables are changed. During the modeling process, we may decide to change the units of some variables, say from meters to millimeters. If we do, the range of those variables and their size relative to the other variables will both change.

Let α ′ > 0 be the smallest intersecting value of α, that is, f (xk + α ′ pk ) f (xk ) + α ′ c1 ∇ f kT pk . 6a) clearly holds for all step lengths less than α ′ . 55)), there exists α ′′ ∈ (0, α ′ ) such that f (xk + α ′ pk ) − f (xk ) α ′ ∇ f (xk + α ′′ pk )T pk . 10) since c1 < c2 and ∇ f kT pk < 0. 6b). Hence, by our smoothness assumption on f , there is an interval around α ′′ for which the Wolfe conditions hold. Moreover, since 35 36 CHAPTER 3. 7) hold in the same interval. The Wolfe conditions are scale-invariant in a broad sense: Multiplying the objective function by a constant or making an affine change of variables does not alter them.

Compute the gradient and Hessian of f 1 (x) a T x and f 2 (x) x T Ax. 6) for the function cos(1/x) around a nonzero point x, and the third-order Taylor expansion of cos(x) around any point x. Evaluate the second expansion for the specific case of x 1. 5 Consider the function f : IR2 → IR defined by f (x) sequence of iterates {xk } defined by xk 1+ 1 2k ∥x∥2 . Show that the cos k sin k satisfies f (xk+1 ) < f (xk ) for k 0, 1, 2, . . Show that every point on the unit circle 2 1} is a limit point for {xk }.

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Numerical Optimization (2nd Edition) (Springer Series in Operations Research and Financial Engineering) by Stephen J. Wright, Jorge Nocedal


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