We present a modified version of Ye\u27s potential reduction algorithm for linear programming. By using partial updating and incorporating scaling factors based on subsets of variables, we are able to solve linear programs having many more variables than constraints faster than comparable full updating algorithms. Our scheme for choosing scaling factors is motivated by certain asymptotic properties of interior point methods
This paper presents the convergence proof and complexity analysis of an interior-point framework tha...
Following the breakthrough work of Tardos (Oper. Res. '86) in the bit-complexity model, Vavasis and ...
Optimization problems with many more inequality constraints than variables arise in support-vector m...
We provide a survey of interior-point methods for linear programming and its extensions that are bas...
AbstractWe propose a new polynomial potential-reduction method for linear programming, which can als...
An Infeasible-Interior-Point Potential-Reduction Algorithm for Linear Programmin
Consider solving a linear program in standard form where the constraint matrix $A$ is $m imes n$, w...
This paper develops a potential reduction algorithm for solving a linear-programming problem directl...
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...
Potential reduction algorithms have a distinguished role in the area of in-terior point methods for ...
AbstractFor a linear program in which the constraint coefficients vary linearly with the time parame...
In his recent work, Dadush et al. introduced the condition number κ for constraint matrices of linea...
Potential Function Methods For Approximately Solving Linear Programming Problems breaks new ground i...
We propose a linear programming method that is based on active-set changes and proximal-point iterat...
This paper presents the convergence proof and complexity analysis of an interior-point framework tha...
Following the breakthrough work of Tardos (Oper. Res. '86) in the bit-complexity model, Vavasis and ...
Optimization problems with many more inequality constraints than variables arise in support-vector m...
We provide a survey of interior-point methods for linear programming and its extensions that are bas...
AbstractWe propose a new polynomial potential-reduction method for linear programming, which can als...
An Infeasible-Interior-Point Potential-Reduction Algorithm for Linear Programmin
Consider solving a linear program in standard form where the constraint matrix $A$ is $m imes n$, w...
This paper develops a potential reduction algorithm for solving a linear-programming problem directl...
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...
Potential reduction algorithms have a distinguished role in the area of in-terior point methods for ...
AbstractFor a linear program in which the constraint coefficients vary linearly with the time parame...
In his recent work, Dadush et al. introduced the condition number κ for constraint matrices of linea...
Potential Function Methods For Approximately Solving Linear Programming Problems breaks new ground i...
We propose a linear programming method that is based on active-set changes and proximal-point iterat...
This paper presents the convergence proof and complexity analysis of an interior-point framework tha...
Following the breakthrough work of Tardos (Oper. Res. '86) in the bit-complexity model, Vavasis and ...
Optimization problems with many more inequality constraints than variables arise in support-vector m...