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

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(Introduction)
(Introduction)
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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. More specifically, 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.
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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.
  
 
=Uses=
 
=Uses=

Revision as of 14:26, 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.

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