WebThe Wolfe (or strong Wolfe) conditions are among the most widely applicable and useful termination conditions. We now describe in some detail a one-dimensional search procedure that is guaranteed to find a step length satisfying the strong Wolfe conditions (3.7) for any parameters c1and c2 satisfying 0 < c1< c2 < 1. WebFeb 27, 2024 · Our search direction not only satisfies the descent property, but also the sufficient descent condition through the use of the strong Wolfe line search, the global convergence is proved. The numerical comparison shows the efficiency of the new algorithm, as it outperforms both the DY and DL algorithms. 1 Introduction
Wolfe conditions - Wikipedia
WebJul 31, 2006 · The strong Wolfe conditions are usually used in the analyses and implementations of conjugate gradient methods. This paper presents a new version of the conjugate gradient method, which converges globally, provided the line search satisfies the standard Wolfe conditions. The Wolfe conditions can result in a value for the step length that is not close to a minimizer of . If we modify the curvature condition to the following, then i) and iii) together form the so-called strong Wolfe conditions, and force to lie close to a critical point of . Rationale [ edit] See more In the unconstrained minimization problem, the Wolfe conditions are a set of inequalities for performing inexact line search, especially in quasi-Newton methods, first published by Philip Wolfe in 1969. See more Wolfe's conditions are more complicated than Armijo's condition, and a gradient descent algorithm based on Armijo's condition has a better theoretical guarantee than one … See more A step length $${\displaystyle \alpha _{k}}$$ is said to satisfy the Wolfe conditions, restricted to the direction $${\displaystyle \mathbf {p} _{k}}$$, if the following two inequalities hold: with See more • Backtracking line search See more • "Line Search Methods". Numerical Optimization. Springer Series in Operations Research and Financial Engineering. 2006. pp. 30–32. doi:10.1007/978-0-387-40065-5_3. ISBN 978-0-387-30303-1. • "Quasi-Newton Methods". Numerical … See more cherry zebra columbus
Hybrid Riemannian conjugate gradient methods with global
WebOct 26, 2024 · SD: the steepest descent method with a line search satisfying the standard Wolfe conditions . Our numerical experiments indicate that the HS variant considered here outperforms the HS+ method with the strong Wolfe conditions studied in . In the latter work, the authors reported that the HS+ and PRP+ were the most efficient methods among … WebApr 26, 2024 · I'm trying to apply steepest descent satifying strong wolfe conditions to the Rosenbruck function with inital x0=(1.2,1.2), however, although the function itself has a … WebThe goal is to calculate the log of its determinant: log ( det ( K)). This calculation often appears when handling a log-likelihood of some Gaussian-related event. A naive way is to calculate the determinant explicitly and then calculate its log. However, this way is known for its numerical instability (i.e., likely to go to negative infinity). cherry z gamble