WebJun 2, 2024 · They proved that by using scaled vector transport, this hybrid method generates a descent direction at every iteration and converges globally under the strong Wolfe conditions. In this paper, we focus on the sufficient descent condition [ 15] and sufficient descent conjugate gradient method on Riemannian manifolds. 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
LineSearches.jl/strongwolfe.jl at master · JuliaNLSolvers ... - Github
WebDec 9, 2024 · Here, we propose a line search algorithm for finding a step size satisfying the strong Wolfe conditions in the vector optimization setting. Well definedness and finite termination results are provided. We discuss practical aspects related to the algorithm and present some numerical experiments illustrating its applicability. Websatisfying the strong vector-valued Wolfe conditions. At each iteration, our algorithm works with a scalar function and uses an inner solver designed to nd a step-size satisfying the strong scalar-valued Wolfe conditions. In the multiobjective optimization case, such scalar function corresponds to one of the objectives. flights cwb lax
scipy.optimize.line_search — SciPy v1.6.0 Reference Guide
WebJan 28, 2024 · The proposed method is convergent globally with standard Wolfe conditions and strong Wolfe conditions. The numerical results show that the proposed method is promising for a set of given test problems with different starting points. Moreover, the method reduces to the classical PRP method as the parameter q approaches 1. 1 … WebDec 31, 2024 · Find alpha that satisfies strong Wolfe conditions. Parameters fcallable f (x,*args) Objective function. myfprimecallable f’ (x,*args) Objective function gradient. xkndarray Starting point. pkndarray Search direction. gfkndarray, optional Gradient value for x=xk (xk being the current parameter estimate). Will be recomputed if omitted. Webstrong-wolfe-conditions-line-search A line search method for finding a step size that satisfies the strong Wolfe conditions (i.e., the Armijo (i.e., sufficient decrease) condition … flights cx632