In this paper we propose two new line search methods for convex functions. These new methods exploit the convexity property of the function, contrary to existing methods.The worst method is an improved version of the golden section method.For the second method it is proven that after two evaluations the objective gap is at least halved.The practical efficiency of the methods is shown by applying our methods to a real-life bus and buffer size optimization problem and to several classes of convex functions
This paper introduces a modified version of the well known global optimization technique named line ...
Abstract. We discuss the convergence of line search methods for minimization. We explain how Newton’...
In this paper we investigate the effects of replacing the objective function of a 0-1 mixed-integer ...
In this paper, we introduce a new line search technique, then employ it to construct a novel acceler...
In this paper, we propose a line-search procedure for the logarithmic barrier function in the contex...
International audienceIn this paper we present a subgradient method with non-monotone line search fo...
For the past few decades, various algorithms have been proposed to solve convex minimization problem...
Thesis (Ph.D.)--University of Washington, 2017Convex optimization is more popular than ever, with ex...
A quadratically convergent line-search algorithm for piecewise smooth convex optimization based on a...
Abstract. An iterative projection algorithm by adopting Armijo-like line search to solve the convex ...
We consider the gradient (or steepest) descent method with exact line search applied to a strongly c...
We consider the gradient (or steepest) descent method with exact line search applied to a strongly c...
In this paper, we propose a line-search procedure for the logarithmic barrier function in the contex...
We consider the gradient (or steepest) descent method with exact line search applied to a strongly c...
Abstract—In this paper, we propose a novel iterative convex approximation algorithm to efficiently c...
This paper introduces a modified version of the well known global optimization technique named line ...
Abstract. We discuss the convergence of line search methods for minimization. We explain how Newton’...
In this paper we investigate the effects of replacing the objective function of a 0-1 mixed-integer ...
In this paper, we introduce a new line search technique, then employ it to construct a novel acceler...
In this paper, we propose a line-search procedure for the logarithmic barrier function in the contex...
International audienceIn this paper we present a subgradient method with non-monotone line search fo...
For the past few decades, various algorithms have been proposed to solve convex minimization problem...
Thesis (Ph.D.)--University of Washington, 2017Convex optimization is more popular than ever, with ex...
A quadratically convergent line-search algorithm for piecewise smooth convex optimization based on a...
Abstract. An iterative projection algorithm by adopting Armijo-like line search to solve the convex ...
We consider the gradient (or steepest) descent method with exact line search applied to a strongly c...
We consider the gradient (or steepest) descent method with exact line search applied to a strongly c...
In this paper, we propose a line-search procedure for the logarithmic barrier function in the contex...
We consider the gradient (or steepest) descent method with exact line search applied to a strongly c...
Abstract—In this paper, we propose a novel iterative convex approximation algorithm to efficiently c...
This paper introduces a modified version of the well known global optimization technique named line ...
Abstract. We discuss the convergence of line search methods for minimization. We explain how Newton’...
In this paper we investigate the effects of replacing the objective function of a 0-1 mixed-integer ...