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. |
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Saturs
ABOUT DISJUNCTIVE OPTIMIZATION | 45 |
ON REGULARITY AND OPTIMALITY IN NONLINEAR | 59 |
ASYMPTOTIC CONSTRAINT QUALIFICATIONS AND | 75 |
STABILITY OF THE FEASIBLE SET MAPPING IN CON | 101 |
ON CONVEXLOWER LEVEL PROBLEMSINGENERAL | 121 |
ON DUALITY THEORY OF CONIC LINEAR PROBLEMS | 135 |
Conic linear problems | 136 |
Problem of moments | 145 |
R eferences | 194 |
FIRSTORDER ALGORITHMS FOR OPTIMIZATION 197 | 196 |
SemiInfinite MinMax Problems | 199 |
Rate of Convergence of Algorithm 2 2 | 206 |
Minimization of the Maximum Eigenvalue of a Symmetric Matrix | 207 |
Problems with SemiInfinite Constraints | 211 |
Problems with Maximum Eigenvalue Constraints | 216 |
A Numerical Example | 217 |
Semiinfinite programming | 152 |
Continuous linear programming | 155 |
References | 164 |
NUMERICAL METHODS | 166 |
TWO LOGARITHMIC BARRIER METHODS FOR CON | 169 |
A bundle method using esubgradients | 170 |
Description of the barrier method | 172 |
Properties of the method | 175 |
Numerical aspects | 181 |
Numerical example | 182 |
A regularized logbarrier method | 185 |
Numerical results of the regularized method | 191 |
Conclusions | 193 |
Conclusion | 219 |
METHOD FOR LINEAR SEMIINFINITE PROGRAM | 221 |
11 | 235 |
References | 246 |
4 | 252 |
References | 269 |
ON STABILITY OF GUARANTEED ESTIMATION 299 | 298 |
255 | 325 |
OPTIMIZATION UNDER UNCERTAINTY AND LINEAR | 327 |
Conclusions | 340 |
SEMIINFINITE ASSIGNMENT ANDTRANSPORTATION | 349 |
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 - 2001 |
Semi-Infinite Programming: Recent Advances Miguel ngel Goberna,Marco A. L pez Priekšskatījums nav pieejams - 2001 |
Bieži izmantoti vārdi un frāzes
algorithm applied Assume assumption asymptotic Banach space coalition compact compact set compute cone consider constraint qualification convergence Convex Analysis convex functions convex inequality convex programming convex set corresponding defined denote dual problem duality gap equivalent estimation example exists feasible set feasible solution finite number fuzzy numbers fuzzy sets global error bound Hence holds hyperplane implies infinite K. O. Kortanek Lemma linear programming linear programming problem linear semi-infinite programming lower semicontinuous LSIP LTP situations Mathematical Programming measure membership function method minimizer nonempty obtain optimal solution optimal value optimization problem Owen(A parameters primal problem of moments Proof Proposition result saddle point satisfied Section semi-infinite optimization sensors sequence Slater condition solving subset sup-function Theorem 3.1 topology va(N val(CLP val(D val(P vector vp(N weak topology