Titlebook: Stochastic Programming 84, Part II; Andras Prékopa,Roger J.- B. Wets Book 1986Latest edition Springer-Verlag Berlin Heidelberg 1986 algori

MOTE

书目名称Stochastic Programming 84, Part II影响因子(影响力)




书目名称Stochastic Programming 84, Part II影响因子(影响力)学科排名




书目名称Stochastic Programming 84, Part II网络公开度




书目名称Stochastic Programming 84, Part II网络公开度学科排名




书目名称Stochastic Programming 84, Part II被引频次




书目名称Stochastic Programming 84, Part II被引频次学科排名




书目名称Stochastic Programming 84, Part II年度引用




书目名称Stochastic Programming 84, Part II年度引用学科排名




书目名称Stochastic Programming 84, Part II读者反馈




书目名称Stochastic Programming 84, Part II读者反馈学科排名

射手座

A Lagrangian finite generation technique for solving linear-quadratic problems in stochastic progralems correspond to maximizing the dual objective over the convex hull of finitely many dual feasible solutions. An optimizing sequence is produced for the primal problem that converges at a linear rate in the strongly quadratic case. An outer algorithm of augmented Lagrangian type can be used to introduce strongly quadratic terms, if desired.

heartburn

DALLY

挑剔小责

逢迎春日

Decomposing the requirement space of a transporation problem into polyhedral cones,part of the solution procedures for dynamic and stochastic programs. This usually involves a decomposition of the parameter space, a decomposition which is dictated by the optimality criteria. The subregions correspond to (sub) bases of the coefficient matrix. We suggest an efficient procedure to ge

灰姑娘

Multistage stochastic programs with block-separable recourse,ecourse. Examples of applications where this property can be found are given. The equivalence of multistage programs with block-separable recourse and two-stage programs is shown and the algorithmic implications of block-separability are studied.

Mediocre

A Lagrangian finite generation technique for solving linear-quadratic problems in stochastic prograought itself of high dimension, is approximated by a sequence of quadratic programming subproblems whose dimensionality can be kept low. These subproblems correspond to maximizing the dual objective over the convex hull of finitely many dual feasible solutions. An optimizing sequence is produced for

警告

Medicaid