# Difference between revisions of "Interior-point method for LP"

(→Introduction) |
(→Introduction) |
||

Line 4: | Line 4: | ||

Date Presented: May 25, 2014 <br> | Date Presented: May 25, 2014 <br> | ||

=Introduction= | =Introduction= | ||

− | Interior point methods are a type of algorithm that are used in solving both linear and nonlinear convex optimization problems that contain inequalities as constraints. The LP Interior-Point method relies on having a linear programming model with the objective function and all constraints being continuous and twice continuously differentiable. | + | Interior point methods are a type of algorithm that are used in solving both linear and nonlinear convex optimization problems that contain inequalities as constraints. The LP Interior-Point method relies on having a linear programming model with the objective function and all constraints being continuous and twice continuously differentiable. The problem is solved (assuming there IS a solution) either by iteratively solving for Karun-Kush-Tucker (KKT) conditions or to the original problem with equality instead of inequality constraints, and then applying Newton's method to these conditions. |

=Uses= | =Uses= |

## Revision as of 15:33, 25 May 2014

Authors: John Plaxco, Alex Valdes, Wojciech Stojko. (ChE 345 Spring 2014)

Steward: Dajun Yue, Fengqi You

Date Presented: May 25, 2014

## Contents |

# Introduction

Interior point methods are a type of algorithm that are used in solving both linear and nonlinear convex optimization problems that contain inequalities as constraints. The LP Interior-Point method relies on having a linear programming model with the objective function and all constraints being continuous and twice continuously differentiable. The problem is solved (assuming there IS a solution) either by iteratively solving for Karun-Kush-Tucker (KKT) conditions or to the original problem with equality instead of inequality constraints, and then applying Newton's method to these conditions.

# Uses

# Algorithm

# Example

# Conclusion

## Sources

1. R.J. Vanderbei, Linear Programming: Foundations and Extensions (Chp 17-22). Springer, 2008.

2. J. Nocedal, S. J. Wright, Numerical optimization (Chp 14). Springer, 1999.

3. S. Boyd, L. Vandenberghe, Convex Optimization (Chp 11). Cambridge University Press, 2009