Dynamic Planning And Two Or Multistage Programming Under Risk
Dynamic Planning And Two Or Multistage Programming Under Risk
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In our eReader you can find the full English version of the book. Read Dynamic Planning And Two Or Multistage Programming Under Risk Online - link to read the book on full screen. Our eReader also allows you to upload and read Pdf, Txt, ePub and fb2 books. In the Mini eReder on the page below you can quickly view all pages of the book - Read Book Dynamic Planning And Two Or Multistage Programming Under Risk
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-8- Appendix EXAMPLE 1 ; Given M y = f ; f := vector of constants y > with the Moore-Penrose (Generalized)-Inverse M of the matrix M, the set of feasible solutions can be represented by (yj = M*f + [e - M* m] w > 0; w I 0.
If the random variable nature has a continuous density function, we obtain for our example the optimization problem opt " 'i^^i (^1^^ +_/ g(P • S2y^(y^) d^} g(^) means the density functions of the random variable ? := b - A y . The set Tj of feasible solutions y is defined by... T^ = T^n (m^ y^ = b^ - A^ y-*-; y^ > } By using the Moore-Penrose-Inverse we obtain {y2] = m2* [b^ - a2 y'j +[e-m2*m2J„>0 ^ M^* [b^ - A^ y-*"] + [e - M^* M^J w >, the additional restrictions for y to reduce T to T~. If for example 2* 2 M is the left inverse of M they will be reduced to ^2*, 2 > ^2*, 2 1 M b i M Ay T~ is defined by T- = jy' I m' y^ = bl; m2* a2 yl i m2* b^; y^ t o] .
EXAMPLE 2 : For our example with a discrete random variable nature the optinization problem can be written as -9- K y^ ^h ' ^21 y^^ + ••• + Pq • S2Q y^"^ -♦^p'^' 21 A^^ y' + M^^ y^l = b" A^^'y^ t '.
What to read after Dynamic Planning And Two Or Multistage Programming Under Risk?
You can find similar books in the "Read Also" column, or choose other free books by Michael Werner to read onlineMoreLess
To quickly assess the difficulty of the text, read a short excerpt:
-8- Appendix EXAMPLE 1 ; Given M y = f ; f := vector of constants y > with the Moore-Penrose (Generalized)-Inverse M of the matrix M, the set of feasible solutions can be represented by (yj = M*f + [e - M* m] w > 0; w I 0.
If the random variable nature has a continuous density function, we obtain for our example the optimization problem opt " 'i^^i (^1^^ +_/ g(P • S2y^(y^) d^} g(^) means the density functions of the random variable ? := b - A y . The set Tj of feasible solutions y is defined by... T^ = T^n (m^ y^ = b^ - A^ y-*-; y^ > } By using the Moore-Penrose-Inverse we obtain {y2] = m2* [b^ - a2 y'j +[e-m2*m2J„>0 ^ M^* [b^ - A^ y-*"] + [e - M^* M^J w >, the additional restrictions for y to reduce T to T~. If for example 2* 2 M is the left inverse of M they will be reduced to ^2*, 2 > ^2*, 2 1 M b i M Ay T~ is defined by T- = jy' I m' y^ = bl; m2* a2 yl i m2* b^; y^ t o] .
EXAMPLE 2 : For our example with a discrete random variable nature the optinization problem can be written as -9- K y^ ^h ' ^21 y^^ + ••• + Pq • S2Q y^"^ -♦^p'^' 21 A^^ y' + M^^ y^l = b" A^^'y^ t '.
What to read after Dynamic Planning And Two Or Multistage Programming Under Risk?
You can find similar books in the "Read Also" column, or choose other free books by Michael Werner to read onlineMoreLess
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