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An indefinite quadratic has a unique boundary trust-region minimizer

Example

Take n=2, Δ=1, B=diag⁡(−1,1), and g=(−1,−3)/2. The model Hessian is indefinite. The exact global trust-region minimizer is a unique boundary point, while the Cauchy step is a different feasible boundary point with a strictly smaller predicted decrease.

Facts & Assumptions

[F1]

The model is m(p)=f(x)+gTp+12pTBp and feasibility is ∥p∥2≤1 (Quadratic trust-region models and the actual-to-predicted reduction ratio).

[F2]

A feasible p satisfying the shifted-Hessian equation, positive semidefiniteness, and multiplier complementarity is a global minimizer (Global trust-region minimizers satisfy the shifted-Hessian conditions, including the hard case).

[F3]

For gTBg>0, the Cauchy step is pC=−min⁡{∥g∥22/(gTBg),Δ/∥g∥2}g (The Cauchy step for a quadratic trust-region model).

Proof

1.1givenF1F2algebra

Set p∗=(1,1)/2. Then ∥p∗∥2=1, (B+2I)p∗=(1,3)/2=−g, and B+2I=diag⁡(1,3) is positive definite. With λ=2, complementarity holds at the boundary, so [F2] gives a global minimizer. For any other feasible z≠p∗, expansion gives m(z)−m(p∗)=12(z−p∗)T(B+2I)(z−p∗)+(1−∥z∥22)>0, because the first term is strictly positive and the second nonnegative; hence the minimizer is unique.

2.1givenF1F3algebra∎

The global minimizer has gTp∗=−2 and p∗TBp∗=0, so its predicted reduction is 2. For the Cauchy step, ∥g∥22=5 and gTBg=4, hence [F3] gives pC=(1,3)/10 and ∥pC∥2=1. Its model value is m(pC)=f(x)−5+2/5, so its predicted reduction is 5−2/5<2; the strict inequality follows from 5<12/5.

Depends on

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