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21
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 597 (24 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available, and that the constraint gradients are sparse. We discuss
Train Driver Scheduling
, 1999
"... This thesis describes research into solving the U.K. train driver scheduling problems, which are very complex compared to the bus or other public transport driver scheduling problems. A set covering approach comprising a shift generation stage followed by a ..."
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Cited by 10 (4 self)
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This thesis describes research into solving the U.K. train driver scheduling problems, which are very complex compared to the bus or other public transport driver scheduling problems. A set covering approach comprising a shift generation stage followed by a
A Column Generation Approach to Bus Driver Scheduling
, 1996
"... This paper outlines an alternative solution method which has been incorporated into a system which originated from IMPACS. Improved results on a selection of real bus driver problems are presented. THE DRIVER SCHEDULING PROBLEM ..."
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Cited by 9 (4 self)
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This paper outlines an alternative solution method which has been incorporated into a system which originated from IMPACS. Improved results on a selection of real bus driver problems are presented. THE DRIVER SCHEDULING PROBLEM
GAMS/MINOS: A Solver for Largescale Nonlinear Optimization
 Problems”, GAMS Development Corporation
, 2002
"... ..."
bonsaiG  Algorithms & Design
, 1999
"... This report describes the implementation of bonsaiG, a program for mixedinteger linear programming (MILP). bonsaiG is a research code, designed to explore the utility and power of arc consistency as a general technique for solving MILP problems, and to provide a foundation for exploring other techn ..."
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Cited by 3 (0 self)
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This report describes the implementation of bonsaiG, a program for mixedinteger linear programming (MILP). bonsaiG is a research code, designed to explore the utility and power of arc consistency as a general technique for solving MILP problems, and to provide a foundation for exploring other techniques. It strives to provide maximum flexibility, control, and robustness, while retaining a reasonable level of efificiency. It implements a LPbased branchandbound algorithm and supports binary, general integer, and continuous variables. The underlying LP is an implementation of a dynamic LP algorithm. The tree exploration strategy is depthfirst with bestfirst backtracking. Selection of the next active subproblem is based on a function which can incorporate the objective function and the integer infeasibility of a subproblem. The branching algorithm allows the specification of priorities for selecting branching variables, and the specification of groups of integer variables which are e...
Alternative methods for representing the inverse of linear programming basis matrices, to appear
 in the Progress in Mathematical Programming 19751989, Special publication, Australian Society of Operational Research, Editor
, 1990
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MINOS 5.51 USER’S GUIDE by
, 1983
"... Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the above sponsors. Reproduction in whole or in part is permitted for any purposes of the United States Government. iiContents Preface to MINO ..."
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Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the above sponsors. Reproduction in whole or in part is permitted for any purposes of the United States Government. iiContents Preface to MINOS 5.51 Preface to MINOS 5.0
LargeScale Computing for Complementarity and Variational Inequalities
, 2010
"... Both complementarity problems and variational inequalities are tools for expressing the equilibrium conditions in diverse engineering and economic systems. They also play an important role in constrained optimization problems, encompassing the optimality conditions for linear and nonlinear programs ..."
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Both complementarity problems and variational inequalities are tools for expressing the equilibrium conditions in diverse engineering and economic systems. They also play an important role in constrained optimization problems, encompassing the optimality conditions for linear and nonlinear programs and are necessary and sufficient conditions for convex problems. This thesis is concerned with enhancing and designing solvers with largescale computing capability to process classes of the above two types of problems respectively. One aspect of this research aims at enhancing the efficiency and reliability of PATH, the most widely used solver for mixed complementarity problems. A key component of the PATH algorithm is solving a series of linear complementary subproblems with a pivotal scheme. Improving the efficiency of the linear system routines (factor, solve, and update) required by the pivotal scheme is the critical computational issue. We incorporate two new options besides the default LUSOL package in PATH for such
Linear Algebra Enhancements to the PATH Solver
, 2009
"... This research aims enhancing the efficiency and reliability of PATH, the most widely used solver for mixed complementarity problems. A key component of the PATH algorithm is solving a series of linear complementary subproblems with a pivotal scheme. Improving the efficiency of the linear system rout ..."
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This research aims enhancing the efficiency and reliability of PATH, the most widely used solver for mixed complementarity problems. A key component of the PATH algorithm is solving a series of linear complementary subproblems with a pivotal scheme. Improving the efficiency of the linear system routines (factor, solve, and update) required by the pivotal method is the critical computational issue. We incorporate two new options besides the default LUSOL package in PATH for such functionalities. One of the options employs the UMFPACK package for factor and solve operations, together with an implementation of a stable and efficient blockLU updating scheme, which leads to a significantly more effective version of PATH for solving many largescale sparse systems. The other option exploits the COINOR utilities enhanced by adapting the linear refinements and scaling schemes used in the COINLP routines, which is effective in solving smallerscale systems but less competitive on largescale cases.