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1 
Implementations and analysis of three parallel branchandbound algorithms for the vertex covering problem / Implementations and analysis of 3 parallel branchandbound algorithms for the vertex covering problem.Zariffa, Nohad. January 1986 (has links)
No description available.

2 
Implementations and analysis of three parallel branchandbound algorithms for the vertex covering problemZariffa, Nohad. January 1986 (has links)
No description available.

3 
Skyline queries in database systems /Fu, Gregory Chung Yin. January 2003 (has links)
Thesis (M. Phil.)Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 5152). Also available in electronic version. Access restricted to campus users.

4 
A branch and bound procedure for the sparse assignment problemWentz, William Russell 05 1900 (has links)
No description available.

5 
Fixedcharge transportation problem: a group theoretic approachKennington, Jeffery Lynn 05 1900 (has links)
No description available.

6 
Continuous and integer generalized flow problemsLangley, Robert Warren 08 1900 (has links)
No description available.

7 
Efficient branch and bound algorithm for the dynamic layout problemJariwala, Anish. January 1995 (has links)
Thesis (M.S.)Ohio University, November, 1995. / Title from PDF t.p.

8 
A branchandbound algorithm for the network diversion problem /Erken, Ozgur. January 2002 (has links) (PDF)
Thesis (M.S. in Operations Research)Naval Postgraduate School, December 2002. / Thesis advisor(s): R. Kevin Wood, Matthew Carlyle. Includes bibliographical references (p. 35). Also available online.

9 
A solution scheme of satisfiability problem by active usage of totally unimodularity property.January 2003 (has links)
by Mei Long. / Thesis (M.Phil.)Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 9398). / Abstracts in English and Chinese. / Table of Contents  p.v / Abstract  p.viii / Acknowledgements  p.x / Chapter 1  Introduction  p.1 / Chapter 1.1  Satisfiability Problem  p.1 / Chapter 1.2  Motivation of the Research  p.1 / Chapter 1.3  Overview of the Thesis  p.2 / Chapter 2  Satisfiability Problem  p.4 / Chapter 2.1  Satisfiability Problem  p.5 / Chapter 2.1.1  Basic Definition  p.5 / Chapter 2.1.2  Phase Transitions  p.5 / Chapter 2.2  History  p.6 / Chapter 2.3  The Basic Search Algorithm  p.8 / Chapter 2.4  Some Improvements to the Basic Algorithm  p.9 / Chapter 2.4.1  Satz by ChuMin Li  p.9 / Chapter 2.4.2  Heuristics and Local Search  p.12 / Chapter 2.4.3  Relaxation  p.13 / Chapter 2.5  Benchmarks  p.14 / Chapter 2.5.1  Specific Problems  p.14 / Chapter 2.5.2  Randomly Generated Problems  p.14 / Chapter 2.6  Software and Internet Information for SAT solving  p.16 / Chapter 2.6.1  Stochastic Local Search Algorithms (incomplete)  p.16 / Chapter 2.6.2  Systematic Search Algorithms (complete)  p.16 / Chapter 2.6.3  Some useful Links to SAT Related Sites  p.17 / Chapter 3  Integer Programming Formulation for Logic Problem  p.18 / Chapter 3.1  SAT Problem  p.19 / Chapter 3.2  MAXSAT Problem  p.19 / Chapter 3.3  Logical Inference Problem  p.19 / Chapter 3.4  Weighted Exact Satisfiability Problem  p.20 / Chapter 4  Integer Programming Formulation for SAT Problem  p.22 / Chapter 4.1  From 3CNF SAT Clauses to ZeroOne IP Constraints  p.22 / Chapter 4.2  Integer Programming Model for 3SAT  p.23 / Chapter 4.3  The Equivalence of the SAT and the IP  p.23 / Chapter 4.4  Example  p.24 / Chapter 5  Integer Solvability of Linear Programs  p.27 / Chapter 5.1  Unimodularity  p.27 / Chapter 5.2  Totally Unimodularity  p.28 / Chapter 5.3  Some Results on Recognition of Linear Solvability of IP  p.32 / Chapter 6  TU Based Matrix Research Results  p.33 / Chapter 6.1  2x2 Matrix's TU Property  p.33 / Chapter 6.2  Extended Integer Programming Model for SAT  p.34 / Chapter 6.3  3x3 Matrix's TU Property  p.35 / Chapter 7  Totally Unimodularity Based BranchingandBound Algorithm  p.38 / Chapter 7.1  Introduction  p.38 / Chapter 7.1.1  Enumeration Trees  p.39 / Chapter 7.1.2  The Concept of Branch and Bound  p.42 / Chapter 7.2  TU Based Branching Rule  p.43 / Chapter 7.2.1  How to sort variables based on 2x2 submatrices  p.43 / Chapter 7.2.2  How to sort the rest variables  p.45 / Chapter 7.3  TU Based Bounding Rule  p.46 / Chapter 7.4  TU Based BranchandBound Algorithm  p.47 / Chapter 7.5  Example  p.49 / Chapter 8  Numerical Result  p.57 / Chapter 8.1  Experimental Result  p.57 / Chapter 8.2  Statistical Results of ILOG CPLEX  p.59 / Chapter 9  Conclusions  p.61 / Chapter 9.1  Contributions  p.61 / Chapter 9.2  Future Work  p.62 / Chapter A  The Coefficient Matrix A for Example in Chapter 7  p.64 / Chapter B  The Detailed Numerical Information of Solution Process for Exam ple in Chapter 7  p.66 / Chapter C  Experimental Result  p.67 / Chapter C.1  "# of variables: 20, # of clauses: 91"  p.67 / Chapter C.2  "# of variables: 50, # of clauses: 218"  p.70 / Chapter C.3  # of variables: 75，# of clauses: 325  p.73 / Chapter C.4  "# of variables: 100, # of clauses: 430"  p.76 / Chapter D  Experimental Result of ILOG CPLEX  p.80 / Chapter D.1  # of variables: 20´ة # of clauses: 91  p.80 / Chapter D.2  # of variables: 50，#of clauses: 218  p.83 / Chapter D.3  # of variables: 75，# of clauses: 325  p.86 / Chapter D.4  "# of variables: 100, # of clauses: 430"  p.89 / Bibliography  p.93

10 
Network models with generalized upper bound side constraintsBolouri, Maryam 27 July 1989 (has links)
The objective of this thesis is to develop and
computationally test a new algorithm for the class of
network models with generalized upper bound (GUB) side
constraints. Various algorithms have been developed to
solve the network with arbitrary side constraints problem;
however, no algorithm that exploits the special structure
of the GUB side constraints previously existed. The
proposed algorithm solves the network with GUB side
constraints problem using two sequences of problems. One
sequence yields a lower bound on the optimal value for the
problem by using a Lagrangean relaxation based on relaxing
copies of some subset of the original variables. This is
achieved by first solving a pure network subproblem and
then solving a set of single constraint bounded variable
linear programs. Because only the cost coefficients
change from one pure network subproblem to another, the
optimal solution for one subproblem is at least feasible,
if not optimal, for the next pure network subproblem. The
second sequence yields an upper bound on the optimal value
by using a decomposition of the problem based on changes
in the capacity vector. Solving for the decomposed
problem corresponds to solving for pure network
subproblems that have undergone changes in lower and/or
upper bounds. Recently developed reoptimization
techniques are incorporated in the algorithm to find an
initial (artificial) feasible solution to the pure network
subproblem.
A program is developed for solving the network with
GUB side constraints problem by using the relaxation and
decomposition techniques. The algorithm has been tested
on problems with up to 200 nodes, 2000 arcs and 100 GUB
constraints. Computational experience indicates that the
upper bound procedure seems to perform well; however, the
lower bound procedure has a fairly slow convergence rate.
It also indicates that the lower bound step size, the
initial lower bound value, and the lower and upper bound
iteration strategies have a significant effect on the
convergence rate of the lower bound algorithm. / Graduation date: 1990

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