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L10 Optimal Design L.Multipliers. Homework Review Meaning & of Lagrange Multiplier Summary. Homework 4.44. Now is a “minimize”. We have only used “necessary conditions” We cannot yet conclude that the pt is a MIN!. 4.44 cont’d.
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L10 Optimal Design L.Multipliers • Homework • Review • Meaning & of Lagrange Multiplier • Summary
Homework 4.44 Now is a “minimize” We have only used “necessary conditions” We cannot yet conclude that the pt is a MIN!
4.44 cont’d H(x) is negative definite, therefore the candidate pt is not a local min. (therefore Pt A is NOT a max of the original F(x)). Unbounded?
Gaussian Elimination Case 2 x R1 by -1 + to R2 x R3 by -13 + to R2
Gaussian Elmination 4.57 Case 1 u=0 +R1 to R2 x R2 by -1/2 + to R3 Check feasibility Backsub Using R2
Gaussian Elmination 4.57 Case 2 s=0 +R3 to R4 Check feasibility Backsub Using R3
Gaussian Elmination 4.59 Case 4 s1,2=0 +R3 to R4 Backsub Using R3 Check feasibility Both s1 and s2 =0
Prob 4.59 Where is: Case 1 Case 2 Case 3 Case 4
KKT Necessary Conditions for Min Regularity check - gradients of active inequality constraints are linearly independent
Constraint Variation Sensitivity Theorem The instantaneous rate of change in the objective function with respect to relaxing a constraint IS the LaGrange multiplier!
Practical Use of Multipliers in 4.59 The first-order approximation on f(x), of relaxing a constraint is obtained from a Taylor Series expansion: f(actual)=1 versus f(approx)=0
Summary • Min =-Max, i.e. f(x)=-F(x) • Necessary Conditions for Min • KKT point is a CANDIDATE min! (need sufficient conditions for proof) • Use switching conditions, Gaussian Elimination to find KKT pts • LaGrange multipliers are the instantaneous rate of change in f(x) w.r.t. change in constraint relaxation.