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

The relative merits of using sequential unconstrained methods for solving: minimizef(x) subject togi(x) ⩾ 0, i = 1, ⋯, m, hj(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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