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  • Linear Programming  (1)
  • Stochastic Optimization with Recourse  (1)
  • 1
    Digitale Medien
    Digitale Medien
    Springer
    BIT 27 (1987), S. 50-61 
    ISSN: 1572-9125
    Schlagwort(e): 15A39 ; 65K05 ; Linear Programming ; Simplex Algorithm ; Canonical Bases
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Mathematik
    Notizen: Abstract The Simplex primal and dual methods, for the solution of $$\max \left\{ {c^T x:Ax = b, x \geqslant 0} \right\},$$ were presented previously in terms of certain bases ℤ and $$\mathbb{Y}$$ ofN(A) andR(A T ) respectively. In these implementations, called the ℤ-Simplex Algorithm and the $$\mathbb{Y}$$ -Dual Method, the bases ℤ and $$\mathbb{Y}$$ (giving the edges of the polyhedron in question at the given basic feasible solution) are updated at each iteration. In this paper we show that only partial updates of ℤ are needed in the ℤ-Simplex Algorithm, analogously to the partial updates in the Revised Simplex Algorithm. Similar results can be given for the $$\mathbb{Y}$$ -Dual Method.
    Materialart: Digitale Medien
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    Digitale Medien
    Digitale Medien
    Springer
    Mathematical methods of operations research 46 (1997), S. 51-85 
    ISSN: 1432-5217
    Schlagwort(e): Stochastic Optimization with Recourse ; Decision-making under Uncertainty ; Certainty Equivalents ; Risk Aversion ; Inventory Control ; Insurance
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Mathematik , Wirtschaftswissenschaften
    Notizen: Abstract A random variable (RV) X is given aminimum selling price (S) $$S_U \left( X \right): = \mathop {\sup }\limits_x \left\{ {x + EU\left( {X - x} \right)} \right\}$$ and amaximum buying price (B) $$B_p \left( X \right): = \mathop {\inf }\limits_x \left\{ {x + EP\left( {X - x} \right)} \right\}$$ whereU(·) andP(·) are appropriate functions. These prices are derived from considerations ofstochastic optimization with recourse, and are calledrecourse certainty equivalents (RCE's) of X. Both RCE's compute the “value” of a RV as an optimization problem, and both problems (S) and (B) have meaningful dual problems, stated in terms of theCsiszár φ-divergence $$I_\phi \left( {p,q} \right): = \sum\limits_{i = 1}^n {q_i \phi \left( {\frac{{p_i }}{{q_i }}} \right)} $$ a generalized entropy function, measuring the distance between RV's with probability vectors p and q. The RCES U was studied elsewhere, and applied to production, investment and insurance problems. Here we study the RCEB P, and apply it to problems ofinventory control (where the attitude towards risk determines the stock levels and order sizes) andoptimal insurance coverage, a problem stated as a game between the insurance company (setting the premiums) and the buyer of insurance, maximizing the RCE of his coverage.
    Materialart: Digitale Medien
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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