11 
Higherorder functional languages and intensional logicRondogiannis, Panagiotis 10 April 2015 (has links)
Graduate

12 
Towards next generation logic programming systems /Bansal, Ajay, January 2007 (has links)
Thesis (Ph.D.)University of Texas at Dallas, 2007. / Includes vita. Includes bibliographical references (leaves 105110)

13 
Multistage quadratic stochastic programming /Lau, Karen Karman. January 1999 (has links)
Thesis (Ph. D.)University of New South Wales, 1999. / Also available online.

14 
Optimizing multiship, multimission operational planning for the Joint Force Maritime Component Commander.Silva, Robert A. January 2009 (has links) (PDF)
Thesis (M.S. in Operations Research)Naval Postgraduate School, March 2009. / Thesis Advisor(s): Carlyle, W. Matthew. "March 2009." Description based on title screen as viewed on April 24, 2009. Author(s) subject terms: Integer Programming, Operational Planning, Navy Mission Planner, Navy AssetMission Pairing, Maritime Headquarters, Maritime Operations Center, Constrained Enumeration, Stackbased Enumeration, Mathematical Programming, Optimization, Decision Aid, Planning Tool, Ship Employment Schedule. Includes bibliographical references (p. 6162). Also available in print.

15 
Improving banchandprice algorithms and applying them to Stochastic programs /Silva, Eduardo Ferreira. January 2004 (has links) (PDF)
Thesis (Ph. D. in Operations Research)Naval Postgraduate School, Sept. 2004. / Thesis Advisor(s): R. Kevin Wood. Includes bibliographical references (p. 7180). Also available online.

16 
Towards a semantics bridge between structured specifications and logic specifications /Leung, Pinghung, Karl Richard. January 1992 (has links)
Thesis (M. Phil.)University of Hong Kong, 1992.

17 
Pairing inequalities and stochastic lotsizing problems a study in integer programming /Guan, Yongpei. January 2005 (has links)
Thesis (Ph. D.)Industrial and Systems Engineering, Georgia Institute of Technology, 2006u. / Nemhauser, George L., Committee Chair ; Ahmed, Shabbir, Committee Member ; Bartholdi, John J., Committee Member ; Takriti, Samer, Committee Member ; Gu, Zonghao, Committee Member.

18 
Quadratic programming : quantitative analysis and polynomial running time algorithmsBoljunčić, Jadranka January 1987 (has links)
Many problems in economics, statistics and numerical analysis can be formulated as the optimization of a convex quadratic function over a polyhedral set. A polynomial algorithm for solving convex quadratic programming problems was first developed by Kozlov at al. (1979). Tardos (1986) was the first to present a polynomial
algorithm for solving linear programming problems in which the number of arithmetic steps depends only on the size of the numbers in the constraint matrix and is independent of the size of the numbers in the right hand side and the cost coefficients. In the first part of the thesis we extended Tardos' results to strictly convex quadratic programming of the form max {cTx½xTDx : Ax ≤ b, x ≥0} with D being symmetric positive definite matrix. In our algorithm the number of arithmetic steps is independent of c and b but depends on the size of the entries of the matrices A and D.
Another part of the thesis is concerned with proximity and sensitivity of integer and mixedinteger quadratic programs. We have shown that for any optimal solution z̅ for a given separable quadratic integer programming problem there exist an optimal solution x̅ for its continuous relaxation such that / z̅  x̅ / ∞≤n∆(A) where n is the number of variables and ∆(A) is the largest absolute subdeterminant of the integer constraint matrix A . We have further shown that for any feasible solution z, which is not optimal for the separable quadratic integer programming problem, there exists a feasible solution z̅ having greater objective function value and with / z  z̅ / ∞≤n∆(A). Under some additional assumptions the distance between a pair of optimal solutions to the integer quadratic programming
problem with right hand side vectors b and b', respectively, depends linearly on / b — b' / ₁. The extension to the mixedinteger nonseparable quadratic case is also given.
Some sensitivity analysis results for nonlinear integer programming problems are given. We assume that the nonlinear 0 — 1 problem was solved by implicit enumeration and that some small changes have been made in the right hand side or objective function coefficients. We then established what additional information to keep in the implicit enumeration tree, when solving the original problem, in order to provide us with bounds on the optimal value of a perturbed problem. Also, suppose that after solving the original problem to optimality the problem was enlarged by introducing a new 0 — 1 variable, say xn+1. We determined a lower bound on the added objective function coefficients for which the new integer variable xn+1 remains at zero level in the optimal solution for the modified integer nonlinear program. We discuss the extensions to the mixedinteger case as well as to the case when integer variables are not restricted to be 0 or 1. The computational results for an example with quadratic objective function, linear constraints and 0—1 variables are provided.
Finally, we have shown how to replace the objective function of a quadratic program
with 0—1 variables ( by an integer objective function whose size is polynomially bounded by the number of variables) without changing the set of optimal solutions. This was done by making use of the algorithm given by Frank and Tardos (1985) which in turn uses the simultaneous approximation algorithm of Lenstra, Lenstra and Lovász (1982). / Business, Sauder School of / Graduate

19 
General solution methods for mixed integer quadratic programming and derivative free mixed integer nonlinear programming problemsNewby, Eric 29 July 2013 (has links)
A dissertation submitted to the Faculty of Science School of Computational and Applied Mathematics, University of the Witwatersrand,
Johannesburg. April 27, 2013. / In a number of situations the derivative of the objective function of an optimization
problem is not available. This thesis presents a novel algorithm
for solving mixed integer programs when this is the case. The algorithm
is the first developed for problems of this type which uses a trust region
methodology. Three implementations of the algorithm are developed and
deterministic proofs of convergence to local minima are provided for two of
the implementations.
In the development of the algorithm several other contributions are made.
The derivative free algorithm requires the solution of several mixed integer
quadratic programming subproblems and novel methods for solving nonconvex
instances of these problems are developed in this thesis. Additionally,
it is shown that the current definitions of local minima for mixed integer programs
are deficient and a rigorous approach to developing possible definitions
is proposed. Using this approach we propose a new definition which improves
on those currently used in the literature.
Other components of this thesis are an overview of derivative based mixed
integer nonlinear programming, extensive reviews of mixed integer quadratic
programming and deterministic derivative free optimization and extensive
computational results illustrating the effectiveness of the contributions mentioned
in the previous paragraphs.

20 
Surrogate dual search in nonlinear integer programming.January 2009 (has links)
Wang, Chongyu. / Thesis (M.Phil.)Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 7478). / Abstract also in Chinese. / Abstract  p.1 / Abstract in Chinese  p.3 / Acknowledgement  p.4 / Contents  p.5 / List of Tables  p.7 / List of Figures  p.8 / Chapter 1.  Introduction  p.9 / Chapter 2.  Conventional Dynamic Programming  p.15 / Chapter 2.1.  Principle of optimality and decomposition  p.15 / Chapter 2.2.  Backward dynamic programming  p.17 / Chapter 2.3.  Forward dynamic programming  p.20 / Chapter 2.4.  Curse of dimensionality  p.23 / Chapter 3.  Surrogate Constraint Formulation  p.26 / Chapter 3.1.  Surrogate constraint formulation  p.26 / Chapter 3.2.  Singly constrained dynamic programming  p.28 / Chapter 3.3.  Surrogate dual search  p.29 / Chapter 4.  Distance Confined Path Algorithm  p.34 / Chapter 4.1.  Yen´ةs algorithm for the kth shortest path problem  p.35 / Chapter 4.2.  Application of Yen´ةs method to integer programming  p.36 / Chapter 4.3.  Distance confined path problem  p.42 / Chapter 4.4.  Application of distance confined path formulation to integer programming  p.50 / Chapter 5.  Convergent Surrogate Dual Search  p.59 / Chapter 5.1.  Algorithm for convergent surrogate dual search  p.62 / Chapter 5.2.  "Solution schemes for (Pμ{αk,αβ)) and f(x) = αk"  p.63 / Chapter 5.3.  Computational Results and Analysis  p.68 / Chapter 6.  Conclusions  p.72 / Bibliography  p.74

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