scholarly journals A class of nonmonotone trust region algorithm for solving unconstrained nonlinear optimization problems

ScienceAsia ◽  
2018 ◽  
Vol 44 (1) ◽  
pp. 27
Author(s):  
Fulan Ye ◽  
Yang You ◽  
Zhen Chen ◽  
Baoguo Chen
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Trust Region ◽  
Trust Region Algorithm ◽  
Unconstrained Nonlinear Optimization

scholarly journals A Nonmonotone Weighting Self-Adaptive Trust Region Algorithm for Unconstrained Nonconvex Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Yunlong Lu ◽  
Weiwei Yang ◽  
Wenyu Li ◽  
Xiaowei Jiang ◽  
Yueting Yang
Keyword(s):  
Nonconvex Optimization ◽  
Line Search ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Search Technique ◽  
Trust Region Algorithm ◽  
Unconstrained Optimization Problems ◽  
Line Search Technique ◽  
Self Adaptive

A new trust region method is presented, which combines nonmonotone line search technique, a self-adaptive update rule for the trust region radius, and the weighting technique for the ratio between the actual reduction and the predicted reduction. Under reasonable assumptions, the global convergence of the method is established for unconstrained nonconvex optimization. Numerical results show that the new method is efficient and robust for solving unconstrained optimization problems.

Parameterize the Center of the Trust Region for Solving Quadratic Interpolation Models

2011 ◽  
Vol 52-54 ◽  
pp. 920-925
Author(s):  
Qing Hua Zhou ◽  
Yan Geng ◽  
Ya Rui Zhang ◽  
Feng Xia Xu
Keyword(s):  
Optimization Problems ◽  
Trust Region Method ◽  
Main Idea ◽  
Trust Region ◽  
Search Region ◽  
Trust Region Algorithm ◽  
Unconstrained Optimization Problems ◽  
Simplex Search ◽  
Quadratic Interpolation ◽  
Derivative Free

The derivative free trust region algorithm was considered for solving the unconstrained optimization problems. This paper introduces a novel methodology that modified the center of the trust region in order to improve the search region. The main idea is parameterizing the center of the trust region based on the ideas of multi-directional search and simplex search algorithms. The scope of the new region was so expanded by introducing a parameter as to we can find a better descent directions. Experimental results reveal that the new method is more effective than the classic trust region method on the testing problems.

scholarly journals A Nonmonotone Trust Region Algorithm Based on the Average of the Successive Penalty Function Values for Nonlinear Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Zhensheng Yu ◽  
Jinhong Yu
Keyword(s):  
Global Convergence ◽  
Penalty Function ◽  
Optimization Problems ◽  
Trust Region ◽  
Trust Region Methods ◽  
Constrained Optimization Problems ◽  
Trust Region Algorithm ◽  
Numerical Tests ◽  
Equality Constrained Optimization ◽  
Nonlinear Equality

We present a nonmonotone trust region algorithm for nonlinear equality constrained optimization problems. In our algorithm, we use the average of the successive penalty function values to rectify the ratio of predicted reduction and the actual reduction. Compared with the existing nonmonotone trust region methods, our method is independent of the nonmonotone parameter. We establish the global convergence of the proposed algorithm and give the numerical tests to show the efficiency of the algorithm.

scholarly journals A Novel Self-Adaptive Trust Region Algorithm for Unconstrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yunlong Lu ◽  
Wenyu Li ◽  
Mingyuan Cao ◽  
Yueting Yang
Keyword(s):  
Objective Function ◽  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Convergence Properties ◽  
Trust Region Algorithm ◽  
Unconstrained Optimization Problems ◽  
Piecewise Function ◽  
Self Adaptive

A new self-adaptive rule of trust region radius is introduced, which is given by a piecewise function on the ratio between the actual and predicted reductions of the objective function. A self-adaptive trust region method for unconstrained optimization problems is presented. The convergence properties of the method are established under reasonable assumptions. Preliminary numerical results show that the new method is significant and robust for solving unconstrained optimization problems.

A new adaptive trust region algorithm for optimization problems

2018 ◽  
Vol 38 (2) ◽  
pp. 479-496 ◽  
Author(s):  
Zhou SHENG ◽  
Gonglin YUAN ◽  
Zengru CUI
Keyword(s):  
Optimization Problems ◽  
Trust Region ◽  
Trust Region Algorithm

Fundamentals of Nonlinear Optimization

1995 ◽  
Author(s):  
Christodoulos A. Floudas
Keyword(s):  
Optimality Conditions ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Nonlinear Function ◽  
Nonlinear Least Squares ◽  
Sufficient Optimality Conditions ◽  
Science And Engineering ◽  
Chemical Phase ◽  
Unconstrained Nonlinear Optimization ◽  
Necessary And Sufficient

This chapter discusses the fundamentals of nonlinear optimization. Section 3.1 focuses on optimality conditions for unconstrained nonlinear optimization. Section 3.2 presents the first-order and second-order optimality conditions for constrained nonlinear optimization problems. This section presents the formulation and basic definitions of unconstrained nonlinear optimization along with the necessary, sufficient, and necessary and sufficient optimality conditions. An unconstrained nonlinear optimization problem deals with the search for a minimum of a nonlinear function f(x) of n real variables x = (x1, x2 , . . . , xn and is denoted as Each of the n nonlinear variables x1, x2 , . . . , xn are allowed to take any value from - ∞ to + ∞. Unconstrained nonlinear optimization problems arise in several science and engineering applications ranging from simultaneous solution of nonlinear equations (e.g., chemical phase equilibrium) to parameter estimation and identification problems (e.g., nonlinear least squares).

COMBINATION ADAPTIVE TRUST REGION METHOD BY NON-MONOTONE STRATEGY FOR UNCONSTRAINED NONLINEAR PROGRAMMING

2011 ◽  
Vol 28 (05) ◽  
pp. 585-600 ◽  
Author(s):  
KEYVAN AMINI ◽  
MASOUD AHOOKHOSH
Keyword(s):  
Nonlinear Programming ◽  
Quadratic Convergence ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Trust Region Algorithm ◽  
Region Method ◽  
Unconstrained Optimization Problems ◽  
Adaptive Technique ◽  
And Function

In this paper, we present a new trust region method for unconstrained nonlinear programming in which we blend adaptive trust region algorithm by non-monotone strategy to propose a new non-monotone trust region algorithm with automatically adjusted radius. Both non-monotone strategy and adaptive technique can help us introduce a new algorithm that reduces the number of iterations and function evaluations. The new algorithm preserves the global convergence and has local superlinear and quadratic convergence under suitable conditions. Numerical experiments exhibit that the new trust region algorithm is very efficient and robust for unconstrained optimization problems.

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