Inexact-Restoration Method with Lagrangian Tangent Decrease and New Merit Function for Nonlinear Programming

2001 ◽  
Vol 111 (1) ◽  
pp. 39-58 ◽  
Author(s):  
J. M. Martínez
Keyword(s):  
Nonlinear Programming ◽  
Merit Function ◽  
Inexact Restoration ◽  
Restoration Method

scholarly journals A filter inexact-restoration method for nonlinear programming

Top ◽  
2008 ◽  
Vol 16 (1) ◽  
pp. 126-146 ◽  
Author(s):  
Cândida Elisa P. Silva ◽  
M. Teresa T. Monteiro
Keyword(s):  
Nonlinear Programming ◽  
Inexact Restoration ◽  
Restoration Method

Non-monotone inexact restoration method for nonlinear programming

2019 ◽  
Vol 76 (3) ◽  
pp. 867-888 ◽  
Author(s):  
Juliano B. Francisco ◽  
Douglas S. Gonçalves ◽  
Fermín S. V. Bazán ◽  
Lila L. T. Paredes
Keyword(s):  
Nonlinear Programming ◽  
Inexact Restoration ◽  
Restoration Method

scholarly journals The demand adjustment problem via inexact restoration method

2020 ◽  
Vol 39 (3) ◽  
Author(s):  
Jorgelina Walpen ◽  
Pablo A. Lotito ◽  
Elina M. Mancinelli ◽  
Lisandro Parente
Keyword(s):  
Adjustment Problem ◽  
Inexact Restoration ◽  
Restoration Method

scholarly journals A metaheuristic penalty approach for the starting point in nonlinear programming

2020 ◽  
Vol 54 (2) ◽  
pp. 451-469
Author(s):  
David R. Penas ◽  
Marcos Raydan
Keyword(s):  
Nonlinear Programming ◽  
Feasible Solution ◽  
Packing Problem ◽  
Merit Function ◽  
Test Problems ◽  
Huge Amount ◽  
Novel Approach ◽  
Starting Point ◽  
Metaheuristic Techniques ◽  
Optimization Schemes

Solving nonlinear programming problems usually involve difficulties to obtain a starting point that produces convergence to a local feasible solution, for which the objective function value is sufficiently good. A novel approach is proposed, combining metaheuristic techniques with modern deterministic optimization schemes, with the aim to solve a sequence of penalized related problems to generate convenient starting points. The metaheuristic ideas are used to choose the penalty parameters associated with the constraints, and for each set of penalty parameters a deterministic scheme is used to evaluate a properly chosen metaheuristic merit function. Based on this starting-point approach, we describe two different strategies for solving the nonlinear programming problem. We illustrate the properties of the combined schemes on three nonlinear programming benchmark-test problems, and also on the well-known and hard-to-solve disk-packing problem, that possesses a huge amount of local-nonglobal solutions, obtaining encouraging results both in terms of optimality and feasibility.

scholarly journals Inexact Restoration Method for Derivative-Free Optimization with Smooth Constraints

2013 ◽  
Vol 23 (2) ◽  
pp. 1189-1213 ◽  
Author(s):  
L. F. Bueno ◽  
A. Friedlander ◽  
J. M. Martínez ◽  
F. N. C. Sobral
Keyword(s):  
Derivative Free Optimization ◽  
Derivative Free ◽  
Inexact Restoration ◽  
Restoration Method ◽  
Smooth Constraints
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