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 70.
4. lappuse
... - infinite linear inequality system appearing in ( 1.1 ) : u Є Rm , u1 P ; ≥ c¿ , for all i Є I. ( 1.2 ) Later , in Definition 4.1 we shall be denoting the 4 SEMI - INFINITE PROGRAMMING . RECENT ADVANCES Introduction: Origins of a theory.
... - infinite linear inequality system appearing in ( 1.1 ) : u Є Rm , u1 P ; ≥ c¿ , for all i Є I. ( 1.2 ) Later , in Definition 4.1 we shall be denoting the 4 SEMI - INFINITE PROGRAMMING . RECENT ADVANCES Introduction: Origins of a theory.
5. lappuse
... denoting the space of such X - functions as R1 . We define a fundamental convex cone with a slight abuse of standard nomenclature . Definition 1.2 The moment cone of ( 1.1 ) is the convex set Mm + 1 = convex cone { ( ~~ ) . Pi U -Ci ΕΙ ...
... denoting the space of such X - functions as R1 . We define a fundamental convex cone with a slight abuse of standard nomenclature . Definition 1.2 The moment cone of ( 1.1 ) is the convex set Mm + 1 = convex cone { ( ~~ ) . Pi U -Ci ΕΙ ...
7. lappuse
... denoting , r = 1 , ... m . Program GLP Let ur , r = 1 , m , and — um + 1 be real valued convex functions defined on an arbitrary convex set S , and let bЄ Rm . Find Z = sup um + 1 ( x ) À for among x E S and AER which satisfy Ur ( x ) ...
... denoting , r = 1 , ... m . Program GLP Let ur , r = 1 , m , and — um + 1 be real valued convex functions defined on an arbitrary convex set S , and let bЄ Rm . Find Z = sup um + 1 ( x ) À for among x E S and AER which satisfy Ur ( x ) ...
11. lappuse
... denote the polynomial ring , R [ 0 ] , consisting of finite degree , real coefficient polynomials in an ... denoted by R ( 0 ) . 4.1 n SEMI - INFINITE PROGRAMMING REGULARIZATIONS WITH INFINITE NUMBERS Introducing an infinity , or a ...
... denote the polynomial ring , R [ 0 ] , consisting of finite degree , real coefficient polynomials in an ... denoted by R ( 0 ) . 4.1 n SEMI - INFINITE PROGRAMMING REGULARIZATIONS WITH INFINITE NUMBERS Introducing an infinity , or a ...
12. lappuse
... denote the m × m identity matrix and em the m - vector of all ones . Let PR ( U ) denote the following program . T vp ( U ) = inf u1 Po subject to u1 Pi > ci , i Є I uTIm > -Uem -uTIm > -Uem VD ( U ) = sup Ziel Cili - Uemm ( v + + v ̄ ) ...
... denote the m × m identity matrix and em the m - vector of all ones . Let PR ( U ) denote the following program . T vp ( U ) = inf u1 Po subject to u1 Pi > ci , i Є I uTIm > -Uem -uTIm > -Uem VD ( U ) = sup Ziel Cili - Uemm ( v + + v ̄ ) ...
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