Difference between revisions of "Interior-point method for LP"
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Claimed by John Plaxco, Alex Valdes, Wojciech Stojko.<br> | Claimed by John Plaxco, Alex Valdes, Wojciech Stojko.<br> | ||
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Revision as of 15:24, 25 May 2014
Claimed by John Plaxco, Alex Valdes, Wojciech Stojko.
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
