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NLPQL
The Fortran subroutine ''NLPQLP'', a newer version of ''NLPQL'', solves smooth nonlinear programming problems by a sequential quadratic programming (SQP) algorithm. The new version is specifically tuned to run under distributed systems. In case of computational errors, caused for example by inaccurate function or gradient evaluations, a non-monotone line search is activated. The code is easily transformed to C by f2c f2c is a program to convert Fortran 77 to C code, developed at Bell Laboratories. The standalone f2c program was based on the core of the first complete Fortran 77 compiler to be implemented, the "f77" program by Feldman and Weinberger. ... and is widely used in academia and industry. References * External links * http://klaus-schittkowski.de/nlpqlp.htm * https://www.schittkowski.de/numericalsoftware_nlpqlp.php Mathematical software {{Compu-library-stub ...
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Sequential Quadratic Programming
Sequential quadratic programming (SQP) is an iterative method for constrained nonlinear optimization. SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable. SQP methods solve a sequence of optimization subproblems, each of which optimizes a quadratic model of the objective subject to a linearization of the constraints. If the problem is unconstrained, then the method reduces to Newton's method for finding a point where the gradient of the objective vanishes. If the problem has only equality constraints, then the method is equivalent to applying Newton's method to the first-order optimality conditions, or Karush–Kuhn–Tucker conditions, of the problem. Algorithm basics Consider a nonlinear programming problem of the form: :\begin \min\limits_ & f(x) \\ \mbox & b(x) \ge 0 \\ & c(x) = 0. \end The Lagrangian for this problem is :\mathcal(x,\lambda,\sigma) = f(x) - \lambda b(x) - \sigma c(x), w ...
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