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Convex programs for solving (3.1)–(3.4a),(3.5)

  • Kurt Marti
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 299)

Abstract

For a given n-vector x let (y,π) denote a solution of (3.1)–(3.3). According to section 2.7 for the transition probability measures Kj, j ∈ R, we have that
$${K^j} = \sum\limits_{i \in R} {{\beta _{ij}}{\varepsilon _{{Z^i}}} = \sum\limits_{i \in S} {{{\tilde \beta }_{ij}}{\varepsilon _{{z^i}}}} } $$
(15)
with \(\sum\limits_{i \in R} {{\beta _{ij}} = \sum\limits_{i \in S} {{{\tilde \beta }_{ij}} = 1,} } \) where \({\beta _{ij}} = \frac{{{\alpha _i}{\pi _{ij}}}}{{{\alpha _j}}},\) i, j ∈ R, and \({\tilde \beta _{ij}} = \frac{{{{\tilde \alpha }_i}{\tau _{ij}}}}{{{\alpha _j}}},{\tilde \alpha _i} = \sum\limits_{{z^t} = {z^i}} {{\alpha _t},{\tau _{ij}} = {\tau _{ij}}\left( \pi \right) = \frac{1}{{{{\tilde \alpha }_i}}}} \sum\limits_{{z^t} = {z^i}} {{\alpha _t}{\pi _{tj}},i \in S} ,j \in R\), see (13); let T=T (π) = (τij).

Keywords

Convex Subset Entropy Maximization Variance Maximization Stochastic Program Convex Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Kurt Marti
    • 1
  1. 1.Fakultät für Luft- und RaumfahrttechnikUniversität der Bundeswehr MünchenNeubibergGermany

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