Garth P. McCormick

The Slacked Unconstrained Minimization Technique for Convex Programming

An algorithm for solving the problem: minimize

The Sequential Unconstrained Minimization Technique (SUMT) Without Parameters

f( x )

The Sequential Unconstrained Minimization Technique for Nonlinear Programing, a Primal-Dual Method

(a convex function) subject to

Computational Algorithm for the Sequential Unconstrained Minimization Technique for Nonlinear Programming

g_i ( x )\geqq 0

Extensions of SUMT for Nonlinear Programming: Equality Constraints and Extrapolation

,

Communications to the Editor—Exponential Forecasting: Some New Variations

i = 1, \cdots ,m

PROGRAMMING UNDER NONLINEAR CONSTRAINTS BY UNCONSTRAINED MINIMIZATION: A PRIMAL-DUAL METHOD,

, each

A Guide to SUMT-Version 4: The Computer Program Implementing the Sequential Unconstrained Minimization Technique for Nonlinear Programming,

g_i

The Variable Reduction Method for Nonlinear Programming

a concave function, is presented. Specifically, the function \[ P\left[ {x,t,r_k } \right] \equiv f( x ) + r_k^{ - 1} \sum {\left[ {g_i ( x ) - t_i } \right]} ^2 \] is minimized over all x, nonnegative t, for a strictly decreasing null sequence

COMPUTER PROGRAM IMPLEMENTING THE SEQUENTIAL UNCONSTRAINED MINIMIZATION TECHNIQUE FOR NONLINEAR PROGRAMMING.

\{ {r_k } \}