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1 
Sufficient conditions and duality in nonsmooth programming problems.January 1984 (has links)
by Yung Wai Lok. / Bibliography: leaves 3637 / Thesis (M.Ph.)Chinese University of Hong Kong, 1984

2 
Method of lagrange multipliers and the KuhnTucker conditionsGupta, Pramod Kumar January 2010 (has links)
Digitized by Kansas Correctional Industries

3 
Nonconvex programming with applications to production and location problemsVaish, Harish 12 1900 (has links)
No description available.

4 
Mathematical programming with conesMassam, Hélène Ménèxia. January 1977 (has links)
No description available.

5 
Optimizing employment of search platforms to counter selfpropelled semisubmersiblesPfeiff, Daniel M. January 2009 (has links) (PDF)
Thesis (M.S. in Operations Research)Naval Postgraduate School, June 2009. / Thesis Advisor(s): Brown, Gerald G. "June 2009." Description based on title screen as viewed on July 14, 2009. Author(s) subject terms: Optimization, Mathematical Programming, SemiSubmersibles, Search and Detection, DefenderAttacker Optimization. Includes bibliographical references (p. 7172). Also available in print.

6 
Die Rolle der wesentlichen Restriktionen bei einem konvexen ProgramOettli, Karl Werner, January 1964 (has links)
Inaug.Diss.Zürich. / Vita. Includes bibliography.

7 
Duality in convex programmingMuir, David Charles William January 1966 (has links)
Problems of minimizing a convex function or maximizing a concave function over a convex set are called convex programming problems. Duality principles relate two problems, one a minimization problem, the other a maximization problem, in such a way that a solution to one implies a solution to the other and that the minimum value of one is equal to the maximum value of the other.
When the functions are linear and the constraint sets are polyhedral, the problems are called linear programming problems. Their duality is wellknown. Certain duality results of linear programming can be extended to convex programming by means of the theory of conjugate convex functions introduced by Fenchel ([1], [2]).
In this thesis the theory of conjugate functions is generalized and applied to convex programming problems. In particular a duality theorem is given for a class of convex programming problems. This theorem is compared with a duality theorem for convex programming problems given by Dorn [3]. / Science, Faculty of / Mathematics, Department of / Graduate

8 
Determination of reservoir daily operation policies by stochastic dynamic programmingTsou, C. Anthony January 1970 (has links)
Reservoir operation policies are often formulated deterministically on the basis of critical flow hydrology. However, if a dynamic river daily flow forecast system is available for the whole season, the forecast information should be fully utilized in reservoir regulation. Given such a forecast system, two approaches to determining optimal daily operation policies for a single purpose flood control reservoir are suggested. Both approaches use stochastic dynamic programming: one involves the minimization of the expected value of flood damages, and the other involves minimizing the probability of occurrence of an undesirable event, which is a flood damage exceeding a certain amount. The probabilistic approach not only offers a set of alternative optimal daily operation policies, but also indicates the probabilities of being able to achieve the objectives, and thus it forms a basis for comparing and evaluating the alternative objectives. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

9 
Mathematical programming with conesMassam, Hélène Ménèxia. January 1977 (has links)
No description available.

10 
Stochastic programs and their value over deterministic programsCorrigall, Stuart January 1998 (has links)
A dissertation submitted to the Faculty of Arts, University of the Witwatersrand,
Johannesburg, in fulfilment of the requirements for the degree of Master of Arts. / Reallife decisionmaking problems can often be modelled by mathematical programs (or
optimization models). It is common for there to be uncertainty about the parameters of
such optimization models. Usually, this uncertainty is ignored and a simplified
deterministic program is obtained. Stochastic programs take account of this uncertainty by
including a probabilistic description of the uncertain parameters in the model. Stochastic
programs are therefore more appropriate or valuable than deterministic programs in many
situations, and this is emphasized throughout the dissertation. The dissertation contains a
development of the theory of stochastic programming, and a number of illustrative
examples are formulated and solved. As a reallife application, a stochastic model for the
unit commitment problem facing Eskom (one of the world's largest producers of electricity)
is formulated and solved, and the solution is compared with that of the current strategy
employed by Eskom. / AC 2018

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