Nonlinear programming and nonsmooth optimization by successive linear programming

1989 ◽  
Vol 43 (1-3) ◽  
pp. 235-256 ◽  
Author(s):  
R. Fletcher ◽  
E. Sainz de la Maza
Keyword(s):  
Linear Programming ◽  
Nonlinear Programming ◽  
Nonsmooth Optimization ◽  
Successive Linear Programming

Nonlinear Optimization by Successive Linear Programming

1982 ◽  
Vol 28 (10) ◽  
pp. 1106-1120 ◽  
Author(s):  
F. Palacios-Gomez ◽  
L. Lasdon ◽  
M. Engquist
Keyword(s):  
Linear Programming ◽  
Nonlinear Optimization ◽  
Successive Linear Programming

scholarly journals Calculation of interval damping ratio under uncertain load in power system

2012 ◽  
Vol 60 (1) ◽  
pp. 151-158
Author(s):  
J. Xing ◽  
C. Chen ◽  
P. Wu
Keyword(s):  
Linear Programming ◽  
Power System ◽  
Optimization Model ◽  
Damping Ratio ◽  
Small Signal ◽  
Small Signal Stability ◽  
Signal Stability ◽  
Successive Linear Programming ◽  
Calculation Results ◽  
Lower Limits

Calculation of interval damping ratio under uncertain load in power system The problem of small-signal stability considering load uncertainty in power system is investigated. Firstly, this paper shows attempts to create a nonlinear optimization model for solving the upper and lower limits of the oscillation mode's damping ratio under an interval load. Then, the effective successive linear programming (SLP) method is proposed to solve this problem. By using this method, the interval damping ratio and corresponding load states at its interval limits are obtained. Calculation results can be used to evaluate the influence of load variation on a certain mode and give useful information for improvement. Finally, the proposed method is validated on two test systems.

Minimax Input Shaper Design Using Linear Programming

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Tarunraj Singh
Keyword(s):  
Linear Programming ◽  
Nonlinear Programming ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Barycentric Coordinates ◽  
Nonlinear Programming Problem ◽  
Dome A ◽  
Input Shaper ◽  
Maximum Value ◽  
The Cost

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.

Application of a New Penalty Function Method to Design Optimization

1977 ◽  
Vol 99 (1) ◽  
pp. 31-36 ◽  
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.

Successive linear programming for ratio goal problems

1987 ◽  
Vol 32 (3) ◽  
pp. 426-434 ◽  
Author(s):  
R. Armstrong ◽  
A. Charnes ◽  
C. Haksever
Keyword(s):  
Linear Programming ◽  
Successive Linear Programming
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