1. The Most Important Concept in Optimization (minimization)  A point is said to be an optimal solution of a unconstrained minimization if there exists no decent direction  A point is said to be an optimal solution of a constrained minimization if there exists no feasible decent direction  There might exist decent direction but move along this direction will leave out the feasible region

2. Decent Direction of  Move alone the decent direction for a certain stepsize will decrease the objective function value i.e.,

3. Feasible Direction of  Move alone the feasible direction from for a certain stepsize will not leave the feasible region i.e., where is the feasible region.

5. Steep Decent with Exact Line Search Start with any. Having stop if (i) Steep Decent Direction : (ii) Finding Stepsize by Exact Line Search :

6. Minimum Principle Let be a convex and differentiable function be the feasible region. Example:

8. Minimization Problem vs. Kuhn-Tucker Stationary-point Problem Findsuch that KTSP: MP: such that

9. Lagrangian Function  For a fixed Let and  If are convex then is convex., if then  Above result is a sufficient condition if is convex.

10. KTSP with Equality Constraints? (Assume are linear functions) KTSP: Find such that

11. KTSP with Equality Constraints KTSP: Findsuch that  Ifandthen is free variable

12. Generalized Lagrangian Function  For fixed, if then Letand  If are convex andis linear then is convex.  Above result is a sufficient condition if is convex.

13. Lagrangian Dual Problem subject to

14. Lagrangian Dual Problem subject to where

15. Weak Duality Theorem Let be a feasible solution of the primal problem and a feasible solution of the dual problem. Then Corollary:

16. Weak Duality Theorem Corollary:If where and, thenand solve the primal and dual problem respectively. In this case,

17. Saddle Point of Lagrangian Let satisfying Then is called The saddle point of the Lagrangian function

18. Dual Problem of Linear Program subject to Primal LP Dual LP subject to ※ All duality theorems hold and work perfectly!

19. Application of LP Duality (I) Farkas ’ Lemma For any matrixand any vector either or but never both.

20. Application of LP Duality (II) LSQ-Normal Equation Always Has a Solution For any matrixand any vector consider Claim: always has a solution.

21. Dual Problem of Strictly Convex Quadratic Program subject to Primal QP With strictly convex assumption, we have Dual QP subject to