Content deleted Content added
→Substitution method: g(x) → p(x); \frac{\partial g}{\partial x} → \frac{\partial p}{\partial x} {objective: to prevent confusion of the interpretation here as an adapted cost function with the interpretation in an equality constraint for g intended elsewhere in the article } |
No edit summary |
||
Line 39:
If all the hard constraints are linear and some are inequalities, but the objective function is quadratic, the problem is a [[quadratic programming]] problem. It can still be solved in polynomial time by the [[ellipsoid method]] if the objective function is [[Convex function|convex]]; otherwise the problem is [[NP hard]].
====KKT conditions====
Allowing inequality constraints, the [[Karush-Kuhn-Tucker conditions|KKT approach]] to nonlinear programming generalizes the method of Lagrange multipliers, which allows only equality constraints.
===Constraint optimization problems===
| |||