Variance Reduction and Objective Function Evaluation in Stochastic Linear Programs

1998 ◽  
Vol 10 (2) ◽  
pp. 236-247 ◽  
Author(s):  
Julia L. Higle
Author(s):  
T. E. Potter ◽  
K. D. Willmert ◽  
M. Sathyamoorthy

Abstract Mechanism path generation problems which use link deformations to improve the design lead to optimization problems involving a nonlinear sum-of-squares objective function subjected to a set of linear and nonlinear constraints. Inclusion of the deformation analysis causes the objective function evaluation to be computationally expensive. An optimization method is presented which requires relatively few objective function evaluations. The algorithm, based on the Gauss method for unconstrained problems, is developed as an extension of the Gauss constrained technique for linear constraints and revises the Gauss nonlinearly constrained method for quadratic constraints. The derivation of the algorithm, using a Lagrange multiplier approach, is based on the Kuhn-Tucker conditions so that when the iteration process terminates, these conditions are automatically satisfied. Although the technique was developed for mechanism problems, it is applicable to any optimization problem having the form of a sum of squares objective function subjected to nonlinear constraints.


1997 ◽  
Vol 101 (1) ◽  
pp. 177-192 ◽  
Author(s):  
Hercules Vladimirou ◽  
Stavros A. Zenios

2016 ◽  
Vol 72 (5) ◽  
pp. 1230-1243 ◽  
Author(s):  
Thiago de Castro Martins ◽  
Marcos de Sales Guerra Tsuzuki ◽  
Erick Dario León Bueno de Camargo ◽  
Raul Gonzalez Lima ◽  
Fernando Silva de Moura ◽  
...  

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