Interior-point method for LP

From optimization
Revision as of 15:23, 25 May 2014 by JohnPlaxco (Talk | contribs)

Jump to: navigation, search

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.



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.






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