AbstractWe propose a new polynomial potential-reduction method for linear programming, which can also be seen as a large-step path-following method. We do an (approximate) linesearch along the Newton direction with respect to Renegar's strictly convex potential function if the iterate is far away from the central trajectory. If the iterate lies close to the trajectory, we update the lower bound for the optimal value. Dependent on this updating scheme, the iteration bound can be proved to be O(nL) or O(nL). Our method differs from the recently published potential-reduction methods in the choice of the potential function and the search direction
In a dynamical systems paradigm, many optimization algorithms are equivalent to applying forward Eul...
AbstractIn this note we show that a simple modification of Ye's “affinely scaled potential reduction...
In this note we show that a simple modification of Ye's "affinely scaled potential reduction" algori...
AbstractWe propose a new polynomial potential-reduction method for linear programming, which can als...
We provide a survey of interior-point methods for linear programming and its extensions that are bas...
We describe a steepest-descent potential reduction method for linear and convex minimization over a ...
We present a modified version of Ye\u27s potential reduction algorithm for linear programming. By us...
This paper is concerned with the problem of following a trajectory from an infeasible "warm sta...
Written for specialists working in optimization, mathematical programming, or control theory. The ge...
We present a full-Newton step feasible interior-point algorithm for linear optimization based on a n...
This paper develops a potential reduction algorithm for solving a linear-programming problem directl...
Cover title.Includes bibliographical references (p. 29-32).Research partially supported by the U.S. ...
An Infeasible-Interior-Point Potential-Reduction Algorithm for Linear Programmin
There are several classes of interior point algorithms that solve linear programming problems in O(V...
This article considers continuous trajectories of the vector fields induced by two different primal-...
In a dynamical systems paradigm, many optimization algorithms are equivalent to applying forward Eul...
AbstractIn this note we show that a simple modification of Ye's “affinely scaled potential reduction...
In this note we show that a simple modification of Ye's "affinely scaled potential reduction" algori...
AbstractWe propose a new polynomial potential-reduction method for linear programming, which can als...
We provide a survey of interior-point methods for linear programming and its extensions that are bas...
We describe a steepest-descent potential reduction method for linear and convex minimization over a ...
We present a modified version of Ye\u27s potential reduction algorithm for linear programming. By us...
This paper is concerned with the problem of following a trajectory from an infeasible "warm sta...
Written for specialists working in optimization, mathematical programming, or control theory. The ge...
We present a full-Newton step feasible interior-point algorithm for linear optimization based on a n...
This paper develops a potential reduction algorithm for solving a linear-programming problem directl...
Cover title.Includes bibliographical references (p. 29-32).Research partially supported by the U.S. ...
An Infeasible-Interior-Point Potential-Reduction Algorithm for Linear Programmin
There are several classes of interior point algorithms that solve linear programming problems in O(V...
This article considers continuous trajectories of the vector fields induced by two different primal-...
In a dynamical systems paradigm, many optimization algorithms are equivalent to applying forward Eul...
AbstractIn this note we show that a simple modification of Ye's “affinely scaled potential reduction...
In this note we show that a simple modification of Ye's "affinely scaled potential reduction" algori...