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

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

Line 3: | Line 3: | ||

=Introduction= | =Introduction= | ||

− | The Interior-Point method relies on having a linear programming model with the objective function being continuous and twice continuously differentiable. | + | 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 15:22, 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

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. T.F. Edgar, D.M. Himmelblau, L.S. Lasdon, Optimization of chemical processes (pp 242-291). McGraw-Hill, 2001

2. Bradley, Hax, and Magnanti, Applied Mathematical Programming (p 413).

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

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

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