A truncated SQP algorithm for large scale nonlinear programming
                        problems

Boggs, P T; Toll, J W; Kearsley, A J

doi:10.6028/nist.ir.4900

1992

DOI: 10.6028/nist.ir.4900

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A truncated SQP algorithm for large scale nonlinear programming problems

P T Boggs¹,

J W Toll²,

A J Kearsley³

Abstract: We consider the inequality constrained nonlinear programming problem and an SQP algorithm for its solution. We are primarily concerned with two aspects of the general procedure, namely, the approximate solution of the quadratic program, and the need for an appropriate merit function. We first describe an (iterative) interior-point method for the quadratic programming subproblem that, no matter when it its terminated, yields a descent direction for a suggested new merit function. An algorithm based on ideas fro… Show more

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1993

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Large-Scale Nonlinear Constrained Optimization

Conn

Gould

Toint

1993

Linear Algebra for Large Scale and Real-Time Applications

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During the past ten years, much progress has been made in the theory and practice of constrained nonlinear optimization. However, considerable obstacles appear when these ideas are applied to large-scale problems. This is important as many real applications require the solution of problems in thousands of unknowns. In some areas, in particular linear programming, considerable progress has been made. But even modest departures into nonlinearity, for example the solution oflarge, general quadratic programs, present considerable challenges. This is apparent when one views the paucity of software for solving such problems. Unsurprisingly, the position does not improve as more drastic forms of nonlinearity are encountered. In this paper, we will try to explain why the difficulties arise, how attempts are being made to overcome them and what the problems are that still remain. AbstractDuring the past ten years, much progress has been made in the theory and practice of constrained nonlinear optimization. However, considerable obstacles appear when these ideas are applied to large-scale problems. This is important as many real applications require the solution of problems in thousands of unknowns. In some areas, in particular linear programming, considerable progress has been made. But even modest departures into nonlinearity, for example the solution of large, general quadratic programs, present considerable challenges. This is apparent when one views the paucity of software for solving such problems. Unsurprisingly, the position does not improve as more drastic forms of nonlinearity are encountered.In this paper, we will try to explain why the difficulties arise, how attempts are being made to overcome them and what the problems are that still remain.

show abstract

Large-Scale Nonlinear Constrained Optimization

Conn

Gould

Toint

1993

Linear Algebra for Large Scale and Real-Time Applications

View full text Add to dashboard Cite

show abstract

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A truncated SQP algorithm for large scale nonlinear programming problems

Cited by 1 publication

References 9 publications

Large-Scale Nonlinear Constrained Optimization

Large-Scale Nonlinear Constrained Optimization

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