Semi-Infinite Programming: Recent AdvancesMiguel Ángel Goberna, Marco A. López Springer Science & Business Media, 2013. gada 11. nov. - 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. |
No grāmatas satura
1.–5. rezultāts no 79.
13. lappuse
... optimal solution is * = 1. We obtain ON THE 1962-1972 DECADE OF SIP : A SUBJECTIVE VIEW 13.
... optimal solution is * = 1. We obtain ON THE 1962-1972 DECADE OF SIP : A SUBJECTIVE VIEW 13.
14. lappuse
Recent Advances Miguel Ángel Goberna, Marco A. López. where the optimal solution is * = 1. We obtain an equivalent pair of dual semi- infinite programs by introducing a differential supporting hyperplane system . Primal min Mt + x Dual ...
Recent Advances Miguel Ángel Goberna, Marco A. López. where the optimal solution is * = 1. We obtain an equivalent pair of dual semi- infinite programs by introducing a differential supporting hyperplane system . Primal min Mt + x Dual ...
16. lappuse
... feasible solution having negative infinite part is u ( 0 ) = ( −0 , 02 ) . This implies that ( 4.2 ) is asymptotically unbounded ( AUBD ) , an example of [ 61 , Theorem 9 ] . Actually , the asymptotic limiting behavior of ( 4.2 ) ...
... feasible solution having negative infinite part is u ( 0 ) = ( −0 , 02 ) . This implies that ( 4.2 ) is asymptotically unbounded ( AUBD ) , an example of [ 61 , Theorem 9 ] . Actually , the asymptotic limiting behavior of ( 4.2 ) ...
21. lappuse
... feasible solution for the finite subsystem of ( 1.2 ) , known to exist by assumption . We obtain the following contradiction . cj \ j 0 ÕTUJ = õ ̃uj = Σ u } P ; λj ≥ Σej dj ≥ 1 . ( 4.20 ) J 5 A CLASSIFICATION OF DUALITY STATES BASED ...
... feasible solution for the finite subsystem of ( 1.2 ) , known to exist by assumption . We obtain the following contradiction . cj \ j 0 ÕTUJ = õ ̃uj = Σ u } P ; λj ≥ Σej dj ≥ 1 . ( 4.20 ) J 5 A CLASSIFICATION OF DUALITY STATES BASED ...
28. lappuse
... solution , which when taken together form an optimal solution to the total problem ( T ) . When strict convexity is not present , then additional information than prices alone must be sent to the divisions in order to insure optimal ...
... solution , which when taken together form an optimal solution to the total problem ( T ) . When strict convexity is not present , then additional information than prices alone must be sent to the divisions in order to insure optimal ...
Saturs
14 | |
References | 34 |
75 | 40 |
2 | 45 |
ASYMPTOTIC CONSTRAINT QUALIFICATIONS | 75 |
VEX SEMIINFINITE PROGRAMMING | 101 |
6 | 120 |
Alexander Shapiro | 135 |
8 | 217 |
ANALYTIC | 221 |
ON SOME APPLICATIONS OF LSIP TO PROBABILITY | 235 |
References | 254 |
References | 269 |
15 | 325 |
Numerical Results | 345 |
207 | 348 |
Citi izdevumi - Skatīt visu
Semi-Infinite Programming: Recent Advances Miguel Ángel Goberna,Marco A. López Ierobežota priekšskatīšana - 2001 |
Semi-Infinite Programming: Recent Advances Miguel ngel Goberna,Marco A. L pez Priekšskatījums nav pieejams - 2001 |
Bieži izmantoti vārdi un frāzes
algorithm Applications Assume assumption asymptotic Banach space Charnes compact sets computation cone consider constraint qualification convergence Convex Analysis convex functions convex inequality convex programming convex set cutting plane defined denote dual problem duality theory equivalent example exists feasible set feasible solution finite number ft(x fuzzy numbers fuzzy sets given global error bound Hence holds hyperplane implies infinite interval iteration K. O. Kortanek Lemma linear programming linear semi-infinite programming lower semicontinuous LSIP Mathematical Programming method minimizer moment problem nonempty norm obtain Operations Research optimal solution optimization problem parameter previsions primal problem of moments Proof Proposition result satisfied Section semi-infinite optimization semi-infinite programming problems sequence solution set solving Step sup-function t₁ Theorem 3.1 topology val(CLP val(D val(P vector weak PLV property αΕΩ ΘΕΩ