Add to ORKGKiwiel and Murty (1996) discuss the convergence properties of a class of steepest descent algorithm for minimizing a continuously differentiable quasiconvex function f on mo. Under mild conditions, we prove that the limit infimum of IIVflXo)1I is zero and that false convergence does not occur even when f is convex
Cover title.Includes bibliographical references (p. 27-31).Research supported by the U.S. Army Resea...
Abstract. This paper develops convergence theory of the gradient projection method by Calamai and Mo...
AbstractA functional f defined on a closed convex subset C of a normed space is to be minimized. It ...
To minimize a continuously differentiable quasiconvex function f : ℝ n →ℝ, Armijo's steepest descent...
When applied to an unconstrained minimization problem with a convex objective, the steepest descent ...
In a recent paper, V.F. Demyanov, S. Gamidov and I. Sivelina developed an algorithm for solving opti...
In this paper, we present some algorithms for unconstrained convex optimization problems. The develo...
This note discusses proofs for convergence of first-order methods based on simple potential-function...
In this article, we study the unconstrained minimization problem\[(P)\,\,\,\min\left\{ f(x):x\in\mat...
Based on the notion of the ε -subgradient, we present a unified technique to establish convergence p...
The steepest descent method has a rich history and is one of the simplest and best known methods for...
The convergence behavior of gradient methods for minimizing convex differentiable functions is one o...
Based on a result by Taylor et al. (J Optim Theory Appl 178(2):455–476, 2018) on the attainable conv...
AbstractThis paper extends the full convergence of the steepest descent method with a generalized Ar...
The Frank-Wolfe algorithm is a popular method for minimizing a smooth convex function $f$ over a com...
Cover title.Includes bibliographical references (p. 27-31).Research supported by the U.S. Army Resea...
Abstract. This paper develops convergence theory of the gradient projection method by Calamai and Mo...
AbstractA functional f defined on a closed convex subset C of a normed space is to be minimized. It ...
To minimize a continuously differentiable quasiconvex function f : ℝ n →ℝ, Armijo's steepest descent...
When applied to an unconstrained minimization problem with a convex objective, the steepest descent ...
In a recent paper, V.F. Demyanov, S. Gamidov and I. Sivelina developed an algorithm for solving opti...
In this paper, we present some algorithms for unconstrained convex optimization problems. The develo...
This note discusses proofs for convergence of first-order methods based on simple potential-function...
In this article, we study the unconstrained minimization problem\[(P)\,\,\,\min\left\{ f(x):x\in\mat...
Based on the notion of the ε -subgradient, we present a unified technique to establish convergence p...
The steepest descent method has a rich history and is one of the simplest and best known methods for...
The convergence behavior of gradient methods for minimizing convex differentiable functions is one o...
Based on a result by Taylor et al. (J Optim Theory Appl 178(2):455–476, 2018) on the attainable conv...
AbstractThis paper extends the full convergence of the steepest descent method with a generalized Ar...
The Frank-Wolfe algorithm is a popular method for minimizing a smooth convex function $f$ over a com...
Cover title.Includes bibliographical references (p. 27-31).Research supported by the U.S. Army Resea...
Abstract. This paper develops convergence theory of the gradient projection method by Calamai and Mo...
AbstractA functional f defined on a closed convex subset C of a normed space is to be minimized. It ...