| Separable Programming: Theory and Methods 2001 Edition Contributor(s): Stefanov, S. M. (Author) |
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ISBN: 0792368827 ISBN-13: 9780792368823 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: May 2001 Annotation: In this book, the author considers separable programming and, in particular, one of its important cases - convex separable programming. Some general results are presented, techniques of approximating the separable problem by linear programming and dynamic programming are considered. Convex separable programs subject to inequality/ equality constraint(s) and bounds on variables are also studied and iterative algorithms of polynomial complexity are proposed. As an application, these algorithms are used in the implementation of stochastic quasigradient methods to some separable stochastic programs. Numerical approximation with respect to I1 and I4 norms, as a convex separable nonsmooth unconstrained minimization problem, is considered as well. Audience: Advanced undergraduate and graduate students, mathematical programming/ operations research specialists. |
| Additional Information |
| BISAC Categories: - Mathematics | Linear & Nonlinear Programming - Mathematics | Applied - Mathematics | Optimization |
| Dewey: 519.76 |
| LCCN: 2001029308 |
| Series: Applied Optimization |
| Physical Information: 0.92" H x 6.44" W x 9.7" (1.42 lbs) 314 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: In this book, the author considers separable programming and, in particular, one of its important cases - convex separable programming. Some general results are presented, techniques of approximating the separable problem by linear programming and dynamic programming are considered. Convex separable programs subject to inequality/ equality constraint(s) and bounds on variables are also studied and iterative algorithms of polynomial complexity are proposed. As an application, these algorithms are used in the implementation of stochastic quasigradient methods to some separable stochastic programs. Numerical approximation with respect to I1 and I4 norms, as a convex separable nonsmooth unconstrained minimization problem, is considered as well. Audience: Advanced undergraduate and graduate students, mathematical programming/ operations research specialists. |