Results 251 to 260 of about 1,887,807 (290)

Nonlinear programming computations

Preprints of papers presented at the 13th national meeting of the Association for Computing Machinery on - ACM '58, 1958
This paper is concerned with two recently developed computational algorithms for the solution of particular classes of nonlinear programming problems.
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Nonlinear Programming: A Historical View

ACM SIGMAP Bulletin, 1982
A historical survey of the origins of nonlinear programming is presented with emphasis placed on necessary conditions for optimality. The mathematical sources for the work of Karush, John, Kuhn, and Tucker are traced and compared. Their results are illustrated by duality theorems for nonlinear programs that antedate the modern development of the ...
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The Status of Nonlinear Programming Software

Operations Research, 1979
Desirable features of software for solving nonlinear optimization problems are discussed, and several available codes for solving NLPs are described in terms of these features. Codes are classified by algorithm type. Addresses where codes may be obtained are given.
Allan D. Waren, Leon S. Lasdon
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On Nonlinear Fractional Programming

Management Science, 1967
The main purpose of this paper is to delineate an algorithm for fractional programming with nonlinear as well as linear terms in the numerator and denominator. The algorithm presented is based on a theorem by Jagannathan (Jagannathan, R. 1966.
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Duality in nonlinear programming

Optimization Letters, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Nonlinear leastpth optimization and nonlinear programming

Mathematical Programming, 1977
Over the past few years a number of researchers in mathematical programming became very interested in the method of the Augmented Lagrangian to solve the nonlinear programming problem. The main reason being that the Augmented Lagrangian approach overcomes the ill-conditioning problem and the slow convergence of the penalty methods.
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Nonlinear Programming: Introduction, Unconstrained Nonlinear Programming, Constrained Nonlinear Programming

2002
Nonlinear Programming (NLP) is the broad area of applied mathematics that addresses optimization problems when nonlinearity in the functions are envolved. In this chapter we introduce the problem, and making reference to the smooth case, we review the standard optimality conditions that are at the basis of most algorithms for its solution.
DI PILLO, Gianni, PALAGI, Laura
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On The Marginal Function in Nonlinear Programming

Mathematics of Operations Research, 1984
For several types of finite or infinite dimensional optimization problems the marginal function (or optimal value function) is characterized by different local approximations such as generalized gradients, generalized directional derivatives, directional Hadamard or Dini derivatives.
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A Decomposable Nonlinear Programming Approach

Operations Research, 1967
Nonlinear, i.e., pseudo-concave, programming algorithms attempt to maximize an objective function f(x) subject to x in a feasible set. Large step algorithms seek this maximum by recursively generating both a sequence of points {xk} and a sequence of directions {sk}.
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An introduction to nonlinear programming—II

Computers & Electrical Engineering, 1976
Abstract Attention is directed to the problem of numerically determining the minimum of a function l when there are no constraints on the minimizing variables y. Assuming that l is differentiable, the minimization problem can be regarded as being essentially equivalent to the problem of solving a system of n equations involving n unknowns (i ...
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