Numerical experience with sequential quadratic programming algorithms for equality constrained nonlinear programming

1989 ◽  
Vol 15 (1) ◽  
pp. 49-63 ◽  
Author(s):  
David F. Shanno ◽  
Kang Hoh Phua
Keyword(s):  
Nonlinear Programming ◽  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Numerical Experience ◽  
Constrained Nonlinear Programming ◽  
Programming Algorithms

A Unified Outline of Nonlinear Programming Algorithms

1985 ◽  
Vol 107 (4) ◽  
pp. 449-453 ◽  
Author(s):  
K. Schittkowski
Keyword(s):  
Nonlinear Programming ◽  
Sequential Quadratic Programming ◽  
Gradient Methods ◽  
Reduced Gradient ◽  
Numerical Performance ◽  
Constrained Nonlinear Programming ◽  
Generalized Reduced Gradient ◽  
Performance Results ◽  
Programming Algorithms ◽  
Reduced Gradient Methods

The four most successful approaches for solving the constrained nonlinear programming problem are the penalty, multiplier, sequential quadratic programming, and generalized reduced gradient methods. A general algorithmic frame will be presented, which realizes any of these methods only by specifying a search direction for the variables, a multiplier estimate, and some penalty parameters in each iteration. This approach allows one to illustrate common mathematical features and, on the other hand, serves to explain the different numerical performance results we observe in practice.

Enhancing Infeasibility Detection Method for Mechanical Design Problems

2013 ◽  
Author(s):  
E. Khorshid ◽  
A. Falah
Keyword(s):  
Nonlinear Programming ◽  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Detection Method ◽  
Mechanical Design ◽  
Starting Point ◽  
Real Performance ◽  
Infeasibility Detection ◽  
Complex Mechanical Systems ◽  
Initial Starting

This paper presents the application of the Multistart point technique in order to enhance a previous existing infeasibility detection method based on Sequential Quadratic Programming (SQP) used for detecting modeling errors by finding the Minimum Intractable Subsystem (MIS) of constraints. This new method showed a great potential in detecting infeasibility without countering the problems of the initial starting point faced by many methods for Nonlinear Programming Problems. The real performance of the anticipated method is demonstrated by solving complex mechanical systems were inconsistency constraints are presented. The proposed method succeeded to find the MIS set of constraints that cause infeasibility in the these models while direct Nonlinear Programming solver, based on Sequential Quadratic Programming only, failed to detect the correct inconsistent constraints.

The Research on Ship Positioning’ Thruster Allocation Based on Evolutionary Algorithm

2011 ◽  
Vol 327 ◽  
pp. 182-185
Author(s):  
Hong Fu Chi
Keyword(s):  
Nonlinear Programming ◽  
Quadratic Programming ◽  
Evolutionary Algorithm ◽  
Performance Index ◽  
Load Balance ◽  
Sequential Quadratic Programming ◽  
Search Time ◽  
Nonlinear Programming Problem ◽  
Improved Performance ◽  
Nonlinear Equality

Ship Positioning’s Thruster Allocation is a nonlinear programming problem with nonlinear equality and inequality. Morgan's direct thruster allocation method is simple and allocation's speed is quick. And Sequential Quadratic Programming, evolutionary algorithm is used to solve more complex performance index and constrains, however, load balance is not considered. In this paper, performance index is improved considering load balance. And evolutionary algorithm is used to solve the problem. Results show that search time is long. In the future, heuristic based on solved problem operator should be used to decrease time of evolutionary algorithm. For example, setting forbidden operator to eliminate unfeasible solutions. Sequential Quadratic Programming could be used to solve the problem presented in this paper with improved performance index.

scholarly journals A comparative study on optimization methods for the constrained nonlinear programming problems

2005 ◽  
Vol 2005 (2) ◽  
pp. 165-173 ◽  
Author(s):  
Ozgur Yeniay
Keyword(s):  
Nonlinear Programming ◽  
Comparative Study ◽  
Sequential Quadratic Programming ◽  
Gradient Methods ◽  
Optimization Methods ◽  
Test Problems ◽  
Reduced Gradient ◽  
Constrained Nonlinear Programming ◽  
Generalized Reduced Gradient ◽  
Reduced Gradient Methods

Constrained nonlinear programming problems often arise in many engineering applications. The most well-known optimization methods for solving these problems are sequential quadratic programming methods and generalized reduced gradient methods. This study compares the performance of these methods with the genetic algorithms which gained popularity in recent years due to advantages in speed and robustness. We present a comparative study that is performed on fifteen test problems selected from the literature.

Sign in / Sign up

Export Citation Format

Share Document