New coefficient of three-term conjugate gradient method for solving unconstrained optimization problems
Published on Dec 1, 2019
· DOI :10.1063/1.5136480
Abstract
References14
Newest
A New Modified Three-Term Conjugate Gradient Method with Sufficient Descent Property and Its Global Convergence
A new modified three-term conjugate gradient (CG) method is shown for solving the large scale optimization problems. The idea relates to the famous Polak-Ribiere-Polyak (PRP) formula. As the numerator of PRP plays a vital role in numerical result and not having the jamming issue, PRP method is not globally convergent. So, for the new three-term CG method, the idea is to use the PRP numerator and combine it with any good CG formula’s denominator that performs well. The new modification of three-t...
New version of the three-term conjugate gradient method based on spectral scaling conjugacy condition that generates descent search direction
In this paper, a general form of three-term conjugate gradient method is presented, in which the search directions simultaneously satisfy the Dai-Liao conjugacy condition and sufficient descent property. In addition, the choice for an optimal parameter is suggested in the sense that the condition number of the iteration matrix could arrives at its minimum, which can be regarded as the inheritance and development of the spectral scaling quasi-Newton equation. Different from the existent methods, ...
A modified conjugate gradient method is presented for solving unconstrained optimization problems, which possesses the following properties: (i) The sufficient descent property is satisfied without any line search; (ii) The search direction will be in a trust region automatically; (iii) The Zoutendijk condition holds for the Wolfe-Powell line search technique; (iv) This method inherits an important property of the well-known Polak-Ribiere-Polyak (PRP) method: the tendency to turn towards the ste...
In this paper, we propose a modified Polak-Ribiere-Polyak (PRP) conjugate gradient method. An attractive property of the proposed method is that the direction generated by the method is always a descent direction for the objective 'function. This property is independent of the line search used. Moreover, if exact line search is used, the method reduces to the ordinary PRP method. Under appropriate conditions, we show that the modified PRP method with Armijo-type line search is globally convergen...
We propose performance profiles — distribution functions for a performance metric — as a tool for benchmarking and comparing optimization software. We show that performance profiles combine the best features of other tools for performance evaluation.
This paper explores the convergence of nonlinear conjugate gradient methods without restarts, and with practical line searches. The analysis covers two classes of methods that are globally convergent on smooth, nonconvex functions. Some properties of the Fletcher–Reeves method play an important role in the first family, whereas the second family shares an important property with the Polak–Ribiere method. Numerical experiments are presented.
Conjugate gradient optimization algorithms depend on the search directions, $\begin{gathered} s^{(1)} = - g^{(1)} , \hfill \\ s^{(k + 1)} = - g^{(k + 1)} + \beta ^{(k)} s^{(k)} ,k \geqslant 1, \hfill \\ \end{gathered} with different methods arising from different choices for the scalar β(k). In this note, conditions are given on β(k) to ensure global convergence of the resulting algorithms.
This paper reviews some of the most successful methods for unconstrained, constrained and nondifferentiable optimization calculations. Particular attention is given to the contribution that theoretical analysis has made to the development of algorithms. It seems that practical considerations provide the main new ideas, and that subsequent theoretical studies give improvements to algorithms, coherence to the subject, and better understanding.
A simulation test methodology was developed to evaluate unconstrained nonlinear optimization computer algorithms. The test technique simulates problems optimization algorithms encounter in practice by employing a repertoire of problems representing various topographies (descending curved valleys, saddle points, ridges, etc.), dimensions, degrees of nonlinearity (e.g., linear to exponential) and minima, addressing them from various randomly generated initial approximations to the solution and rec...
Cited By0
Newest