A projected Lagrangian algorithm and its implementation for sparse nonlinear constraints

1982 ◽  
pp. 84-117 ◽  
Author(s):  
Bruce A. Murtagh ◽  
Michael A. Saunders
Keyword(s):  
Nonlinear Constraints

Mechanical systems with nonlinear constraints

1997 ◽  
Vol 36 (4) ◽  
pp. 979-995 ◽  
Author(s):  
Manuel de León ◽  
Juan C. Marrero ◽  
David Martín de Diego
Keyword(s):  
Mechanical Systems ◽  
Nonlinear Constraints

scholarly journals Contractors and Linear Matrix Inequalities

2015 ◽  
Vol 1 (3) ◽  
Author(s):  
Jeremy Nicola ◽  
Luc Jaulin
Keyword(s):  
Fixed Point ◽  
Large Class ◽  
Linear Matrix Inequalities ◽  
Interior Point ◽  
Interior Point Methods ◽  
Linear Constraints ◽  
Linear Matrix ◽  
Nonlinear Constraints ◽  
Matrix Inequalities ◽  
Convex Constraints

Linear matrix inequalities (LMIs) comprise a large class of convex constraints. Boxes, ellipsoids, and linear constraints can be represented by LMIs. The intersection of LMIs are also classified as LMIs. Interior-point methods are able to minimize or maximize any linear criterion of LMIs with complexity, which is polynomial regarding to the number of variables. As a consequence, as shown in this paper, it is possible to build optimal contractors for sets represented by LMIs. When solving a set of nonlinear constraints, one may extract from all constraints that are LMIs in order to build a single optimal LMI contractor. A combination of all contractors obtained for other non-LMI constraints can thus be performed up to the fixed point. The resulting propogation is shown to be more efficient than other conventional contractor-based approaches.

Trajectory Optimization Applied to Space Rendezvous

2013 ◽  
Vol 756-759 ◽  
pp. 3466-3470
Author(s):  
Xu Min Song ◽  
Qi Lin
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Nonlinear Constraints ◽  
Design Variables ◽  
Adaptive Simulated Annealing ◽  
Simulation Results

The trajcetory plan problem of spece reandezvous mission was studied in this paper using nolinear optimization method. The optimization model was built based on the Hills equations. And by analysis property of the design variables, a transform was put forward , which eliminated the equation and nonlinear constraints as well as decreaseing the problem dimensions. The optimization problem was solved using Adaptive Simulated Annealing (ASA) method, and the rendezvous trajectory was designed.The method was validated by simulation results.

Development and Assessment of a Total Resistance Optimized Bow for the AE 36

1994 ◽  
Vol 31 (02) ◽  
pp. 149-160
Author(s):  
Donald C. Wyatt ◽  
Peter A. Chang
Keyword(s):  
Experimental Data ◽  
Optimization Procedure ◽  
Wave Resistance ◽  
Scale Model ◽  
Optimization Approach ◽  
Nonlinear Constraints ◽  
Total Resistance ◽  
Hull Form ◽  
Final Design ◽  
Resistance Data

A numerically optimized bow design is developed to reduce the total resistance of a 23 000 ton ammunition ship (AE 36) at a speed of 22 knots. An optimization approach using slender-ship theory for the prediction of wave resistance is developed and applied. The new optimization procedure is an improvement over previous optimization methodologies in that it allows the use of nonlinear constraints which assure that the final design remains within practical limits from construction and operational perspectives. Analytic predictions indicate that the AE 36 optimized with this procedure will achieve a 40% reduction in wave resistance and a 33% reduction in total resistance at 22 knots relative to a Kracht elliptical bulb bow design. The optimization success is assessed by the analysis of 25th scale model resistance data collected at the David Taylor Research Center deepwater towing basin. The experimental data indicate that the optimized hull form yields a 51% reduction in wave resistance and a 12% reduction in total resistance for the vessel at 22 knots relative to the Kracht bulb bow design. Similarly encouraging results are also observed when comparisons are made with data collected on two other conventionally designed AE 36 designs.

Nonlinear Optimal Design of Dynamic Mechanical Systems

1992 ◽  
Author(s):  
T. E. Potter ◽  
K. D. Willmert ◽  
M. Sathyamoorthy
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Iteration Process ◽  
Optimization Method ◽  
Linear Constraints ◽  
Sum Of Squares ◽  
Function Evaluation ◽  
Deformation Analysis ◽  
Nonlinear Constraints ◽  
Quadratic Constraints

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.

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