Performance of Broilers Fed Rations Formulated by Stochastic Nonlinear Programming or Linear Programming with a Margin of Safety

The focus of this paper is on the design of robust input shapers where the maximum value of the cost function over the domain of uncertainty is minimized. This nonlinear programming problem is reformulated as a linear programming problem by approximating a n-dimensional hypersphere with multiple hyperplanes (as in a geodesic dome). A recursive technique to approximate a hypersphere to any level of accuracy is developed using barycentric coordinates. The proposed technique is illustrated on the spring-mass-dashpot and the benchmark floating oscillator problem undergoing a rest-to-rest maneuver. It is shown that the results of the linear programming problem are nearly identical to that of the nonlinear programming problem.

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Application of a New Penalty Function Method to Design Optimization

Journal of Engineering for Industry ◽

10.1115/1.3439160 ◽

1977 ◽

Vol 99 (1) ◽

pp. 31-36 ◽

Cited By ~ 22

Author(s):

S. B. Schuldt ◽

G. A. Gabriele ◽

R. R. Root ◽

E. Sandgren ◽

K. M. Ragsdell

Keyword(s):

Linear Programming ◽

Nonlinear Programming ◽

Design Optimization ◽

Penalty Function ◽

Function Method ◽

Test Problems ◽

Penalty Function Method ◽

Method Of Multipliers ◽

Non Linear Programming ◽

Linear Programming Problems

This paper presents Schuldt’s Method of Multipliers for nonlinear programming problems. The basics of this new exterior penalty function method are discussed with emphasis upon the ease of implementation. The merit of the technique for medium to large non-linear programming problems is evaluated, and demonstrated using the Eason and Fenton test problems.

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Unifying the Tank and Hopfield linear programming circuit and the canonical nonlinear programming circuit of Chua and Lin

IEEE Transactions on Circuits and Systems ◽

10.1109/tcs.1987.1086095 ◽

1987 ◽

Vol 34 (2) ◽

pp. 210-214 ◽

Cited By ~ 84

Author(s):

M. Kennedy ◽

L. Chua

Keyword(s):

Linear Programming ◽

Nonlinear Programming

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On Some Aspects of the Examination in Econometrics

Journal of Vasyl Stefanyk Precarpathian National University ◽

10.15330/jpnu.8.3.7-16 ◽

2021 ◽

Vol 8 (3) ◽

pp. 7-16

Author(s):

Symon Serbenyuk

Keyword(s):

Linear Programming ◽

Nonlinear Programming ◽

Mathematical Models ◽

Mathematical Optimization ◽

Input Output ◽

High Quality ◽

Mathematical Economics ◽

Linear And Nonlinear Programming ◽

Scientific Results ◽

Method Of Lagrange Multipliers

Teaching econometrics has been studied by a number of researchers, however, there is little information available on the quality of examination and on simplification of tests for demonstration the high-quality knowledge by students in concrete topics of econometrics or mathematical economics.One can note the following main goals of studying the basics of mathematical economics or econometrics by students: forming the notions of mathematical model and of modeling economic processes and phenomena; understanding a role of using mathematical models for economics research and obtaining scientific results; formatting skills for constructing mathematical models in economics, for solving economics problems by mathematical modeling.The main goal of this paper is to simplify test tasks, is to help to students to demonstrate the high-quality knowledge in certain areas of mathematical economics, and also is to construct a system of testing tasks, in which the emphasis was placed on the knowledge and understanding of an algorithm of solving the problem.In the present paper, to quality examine the student knowledge in the basics of mathematical economics, a certain system of tests was constructed and is considered. The main attention is also given to algorithms and techniques of solving some tasks (problems) of mathematical economics. The following topics of mathematical economics are viewed: constructing mathematical models of linear programming, the input-output model, the Monge-Kantorovich transportation problem, the simplex method of linear programming, the graphic method of linear and nonlinear programming, the method of Lagrange multipliers in mathematical optimization. Some primary basic results of studying linear programming, nonlinear programming, and the input-output model are noted.A new system of tests that satisfies the aim of this paper is modeled. The described tests require less time for solving than usual tasks. Here we do not focus on the repetition of auxiliary mathematical knowledge and arithmetic skills. These tests are simplified versions of standard tasks and help students to demonstrate knowledge in the mentioned topics of mathematical economics. The tasks are focused only on the knowledge of basic formulas, techniques, and connections between mathematical objects, economic systems, and their elements.

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Laplace Distribution And Probabilistic (bi) In Linear Programming Model

Journal of Economics and Administrative Sciences ◽

10.33095/jeas.v17i61.1090 ◽

2011 ◽

Vol 17 (61) ◽

pp. 20

Author(s):

Ali Khaleel Al-zubidi

Keyword(s):

Linear Programming ◽

Nonlinear Programming ◽

Programming Problem ◽

Programming Model ◽

Random Variable ◽

Laplace Distribution ◽

Probabilistic Programming ◽

Economic Agents ◽

Specification Errors ◽

Nonlinear Programming Model

The theory of probabilistic programming may be conceived in several different ways. As a method of programming it analyses the implications of probabilistic variations in the parameter space of linear or nonlinear programming model. The generating mechanism of such probabilistic variations in the economic models may be due to incomplete information about changes in demand, production and technology, specification errors about the econometric relations presumed for different economic agents, uncertainty of various sorts and the consequences of imperfect aggregation or disaggregating of economic variables. In this Research we discuss the probabilistic programming problem when the coefficient bi is random variable with given Laplace distribution.

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Computer Models in Advertising Media Selection

Journal of Marketing Research ◽

10.1177/002224376800500409 ◽

1968 ◽

Vol 5 (4) ◽

pp. 414-424 ◽

Cited By ~ 4

Author(s):

Dennis H. Gensch

Keyword(s):

Dynamic Programming ◽

Linear Programming ◽

Nonlinear Programming ◽

Computer Models ◽

Media Selection ◽

Marginal Analysis ◽

Heuristic Programming

There is a clear dichotomy between the optimizing and the nonoptimizing approaches. The four main methods using the optimizing approach are: (1) linear programming, (2) nonlinear programming, (3) iteration or marginal analysis, and (4) dynamic programming. The two major nonoptimizing approaches are (1) heuristic programming and (2) simulation. One of the known models using each approach is examined in detail. The relative merits of the various approaches are then discussed.

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