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

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=Introduction= | =Introduction= | ||

− | Interior point methods are a type of | + | Interior point methods are a type of algorithm that are used in solving both linear and nonlinear convex optimization problems. More specifically, convex optimization problems that contain inequalities as constraints. The Interior-Point method relies on having a linear programming model with the objective function and all constraints being continuous and twice continuously differentiable. |

=Uses= | =Uses= |

## Revision as of 15:23, 25 May 2014

Claimed by John Plaxco, Alex Valdes, Wojciech Stojko.

Sources 4 and 5 have a chapter each devoted to our topic. Source 3 has a long section of chapters. Other two sources mention it, and the rest of the books do not have the topic.

## Contents |

# Introduction

Interior point methods are a type of algorithm that are used in solving both linear and nonlinear convex optimization problems. More specifically, convex optimization problems that contain inequalities as constraints. The Interior-Point method relies on having a linear programming model with the objective function and all constraints being continuous and twice continuously differentiable.

# 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