## Descent directions and efficient solutions in discretely distributed stochastic programs |

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### Other editions - View all

Descent Directions and Efficient Solutions in Discretely Distributed ... Kurt Marti Limited preview - 2013 |

Descent Directions and Efficient Solutions in Discretely Distributed ... Kurt Marti No preview available - 1988 |

Descent Directions and Efficient Solutions in Discretely Distributed ... Kurt Marti No preview available - 2014 |

### Common terms and phrases

0bviously 0perations Research According to Theorem AJy-bJ AJy=AJx assumptions Consequently consider constraint construction of feasible convex functions convex loss function Convex programs Corollary corresponding D-stationary point defined Definition 4.1 denotes elements extreme point feasible descent directions feasible direction feasible solution y,n fulfills Furthermore given n-vector Hence holds true i«SQ implies integer kernA least one j«R Lemma linear equations linear independent linear submanifold minimize Moreover Note o i«S objective function obtain one-point measure optimal solution point x«D problem programs with recourse Proof random matrix random variables resp respectively rxr matrix satisfies SD-conditions solving stationary relative stochastic approximation stochastic dominance Stochastic linear programs stochastic optimization Stochastic Programs strict inequality sign sublinear function submatrix suppose system of linear Theorem Theorem 2.2 tuple vector x«Rn x€Rn yields z1+bJ-AJy