A Double Parameter Scaled Modified Broyden-Fletcher-Goldfarb-Shanno Method for Unconstrained Optimization
نویسندگان
چکیده
منابع مشابه
Estimating the Parameters of the Negative-Lindley Distribution using Broyden-Fletcher-Goldfarb-Shanno
Problem statement: The Maximum Likelihood Estimation (MLE) technique is the most efficient statistical approach to estimate parameters in a cross-sectional model. Often, MLE gives rise to a set of non-linear systems of equations that need to be solved iteratively using the Newton-Raphson technique. However, in some situations such as in the Negative-Lindley distribution where it involves more t...
متن کاملA New Scaled Hybrid Modified BFGS Algorithms for Unconstrained Optimization
The BFGS methods is a method to solve an unconstrained optimization. Many modification have been done for solving this problems. In this paper, we present a new scaled hybrid modified BFGS. The new scaled hybrid modified BFGS algorithms are proposed and analyzed. The scaled hybrid modified BFGS can improve the number of iterations. Results obtained by the hybrid modified BFGS algorithms are com...
متن کاملA Parallel Quasi-Newton Method for Gaussian Data Fitting
We describe a parallel method for unconstrained optimization based on the quasi-Newton descent method of Broyden, Fletcher, Goldfarb, and Shanno. Our algorithm is suitable for both single-instruction and multiple-instruction parallel architectures and has only linear memory requirements in the number of parameters used to ®t the data. We also present the results of numerical testing on both sin...
متن کاملThe Algorithms of Broyden-CG for Unconstrained Optimization Problems
The conjugate gradient method plays an important role in solving large-scaled problems and the quasi-Newton method is known as the most efficient method in solving unconstrained optimization problems. Therefore, in this paper, the new hybrid 2592 Mohd Asrul Hery Ibrahim et al. method between the conjugate gradient method and the quasi-newton method for solving optimization problem is suggested....
متن کاملOn the Global Convergence of the PERRY-SHANNO Method for Nonconvex Unconstrained Optimization Problems
In this paper, we prove the global convergence of the Perry-Shanno’s memoryless quasi-Newton (PSMQN) method with a new inexact line search when applied to nonconvex unconstrained minimization problems. Preliminary numerical results show that the PSMQN with the particularly line search conditions are very promising.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Studies in Informatics and Control
سال: 2019
ISSN: 1220-1766,1841-429X
DOI: 10.24846/v27i2y201801