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. In general, a problem is assumed to be strictly feasible, and will have a dual optimal that will satisfy Karush-Kuhn-Tucker (KKT) constraints described below. The problem is solved (assuming there IS a solution) either by iteratively solving for KKT conditions or to the original problem with equality instead of inequality constraints, and then applying Newton's method to these conditions.

Last modified

• 11/29/2018

Creator

• John Plaxco
• Alex Valdes
• Wojciech Stojko

DOI

• https://doi.org/10.21985/N2JB2P

Keyword

• Interior Point

Rights statement

• In Copyright