Semi-Infinite Programming: Recent AdvancesMiguel Ángel Goberna, Marco A. López Springer Science & Business Media, 2001. gada 31. okt. - 386 lappuses Semi-infinite programming (SIP) deals with optimization problems in which either the number of decision variables or the number of constraints is finite. This book presents the state of the art in SIP in a suggestive way, bringing the powerful SIP tools close to the potential users in different scientific and technological fields. The volume is divided into four parts. Part I reviews the first decade of SIP (1962-1972). Part II analyses convex and generalised SIP, conic linear programming, and disjunctive programming. New numerical methods for linear, convex, and continuously differentiable SIP problems are proposed in Part III. Finally, Part IV provides an overview of the applications of SIP to probability, statistics, experimental design, robotics, optimization under uncertainty, production games, and separation problems. Audience: This book is an indispensable reference and source for advanced students and researchers in applied mathematics and engineering. |
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1.–5. rezultāts no 33.
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Saturs
References | 34 |
ABOUT DISJUNCTIVE OPTIMIZATION | 57 |
6 | 67 |
ERROR BOUNDS FOR SEMIINFINITE SYSTEMS | 75 |
9 | 79 |
21 | 85 |
References | 95 |
Introduction | 101 |
5678 | 211 |
7 | 216 |
ON SOME APPLICATIONS OF LSIP TO PROBABILITY | 237 |
References | 254 |
ON STABILITY OF GUARANTEED ESTIMATION | 299 |
15 | 325 |
16 | 347 |
219 | 348 |
6 | 120 |
ON DUALITY THEORY OF CONIC LINEAR PROBLEMS | 135 |
ANALYTIC | 179 |
PROBLEMS WITH A MAXIMUM EIGENVALUE | 197 |
4 | 355 |
THE OWEN SET AND THE CORE OF SEMIINFINITE | 365 |
Citi izdevumi - Skatīt visu
Semi-Infinite Programming: Recent Advances Miguel Ángel Goberna,Marco A. López Ierobežota priekšskatīšana - 2013 |
Semi-Infinite Programming: Recent Advances Miguel ngel Goberna,Marco A. L pez Priekšskatījums nav pieejams - 2001 |
Bieži izmantoti vārdi un frāzes
Algorithm 5.1 Applications assume assumption Banach space compact compact set compute cone consider constraint qualification convergence Convex Analysis convex functions convex programming convex set defined denote DLPT dual problem duality equivalent estimation example exists extreme point feasible set feasible solution finite number ft(x fuzzy numbers fuzzy sets given global error bound Hence holds hyperplane infinite iteration K. O. Kortanek Lemma linear inequality system linear programming linear semi-infinite programming lower semicontinuous LSIP LTP situations Mathematical Programming matrix measure membership function method minimizer moment problem nonempty obtain optimal solution optimal value optimization problem parameter primal Proof Proposition result satisfied Section semi-infinite optimization semi-infinite programming problems sequence solution set solving Step strong Slater condition subset sup-function Theorem 3.1 theory topology trajectory val(CLP val(D val(P vector weak PLV property