What is Interior point method?
[[File:Karmarkar.svg|thumb|379x379px|Example solution. The blue lines show the different constraints, the red line shows [[Approximation|approximations]]. each dot in the red line is an approximation step. ]] '''Interior point methods''' are [[Algorithm|algorithms]] to solve certain [[optimization]] problems. They have been used to find solutions in [[linear programming]] and quadratic programming problems. Interior point methods can be used when the solution is a [[convex set]]. The problem can then be restated as minimizing or maximizing a [[linear function]] over this set of solutions. They are called ''interior'' point methods, because they approximate the convex set from the inside. Interior point methods are often used because they have a better runtime behavior than the algorithms commonly used with linear programming. Because the [[sequence]] of values will converge towards a barrier, they are also known as '''barrier methods'''. {{Sci-stub}} [[Category:Combinatorial optimization]]