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Penalty function versus non-penalty function methods for constrained nonlinear programming problems

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Abstract

The relative merits of using sequential unconstrained methods for solving: minimizef(x) subject tog i (x) ⩾ 0, i = 1, ⋯, m, h j (x) = 0, j = 1, ⋯, p versus methods which handle the constraints directly are explored. Nonlinearly constrained problems are emphasized. Both classes of methods are analyzed as to parameter selection requirements, convergence to first and second-order Kuhn-Tucker Points, rate of convergence, matrix conditioning problems and computations required.

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  1. Garth P. McCormick

    Present address: The George Washington University, Washington, D.C., USA

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  1. Research Analysis Corporation, McLean, Virginia, USA

    Garth P. McCormick

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McCormick, G.P. Penalty function versus non-penalty function methods for constrained nonlinear programming problems. Mathematical Programming 1, 217–238 (1971). https://doi.org/10.1007/BF01584087

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