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Exact safeguarded Levenberg-Marquardt iterates approach stationarity on compact sublevels

Statement

Let r:U→Rm be C1 on open U⊆Rn, let ϕ=∥r∥22/2, and fix x0∈U. Suppose the initial sublevel S={x∈U:ϕ(x)≤ϕ(x0)} is compact, its closed unit neighborhood S1={y:dist⁡(y,S)≤1} lies in U, and on S1 the Jacobian J=Dr is L-Lipschitz with ∥J(x)∥2≤M and ∥r(x)∥2≤R0. Fix nonsingular D and constants d,d+>0 such that ∥Dv∥2≥d∥v∥2 and max⁡{∥D∥2,∥DT∥2}≤d+. Choose λmin⁡>0, λ0≥λmin⁡, γ>1, 0<η1<η2<1, and Δ>0, and define CD,CE,P,Λ,N as in Uniformly large damping forces an acceptable LM trial in finitely many attempts. Run Finite-budget Levenberg-Marquardt with a trust-ratio safeguard in exact arithmetic with at least N trials per iteration, a supplied exact reduced QR factorization of each augmented matrix at every trial, exact subproblem solves, and no evaluation failures.

The run either terminates at an iterate with J(xk)Tr(xk)=0, or every nonstationary iteration is accepted and ϕ(xk)−ϕ(xk+1)≥η1c ∥J(xk)Tr(xk)∥22,c:=λmin⁡d2(M2+λmax⁡d+2)2, where λmax⁡:=max⁡{λ0,γΛ}. Consequently ∥J(xk)Tr(xk)∥2→0, and every accumulation point is stationary. This does not imply r(xk)→0; the method certifies first-order stationarity of the least-squares objective, not exact data fit.

Facts & Assumptions

Given: The residual, objective, compact sublevel and neighborhood, Jacobian and residual bounds, scaling matrix, damping parameters, trial cap, and exact-arithmetic LM run in the Statement.

[F1]

Finite-budget Levenberg-Marquardt with a trust-ratio safeguard: a nonstationary exact LM trial is accepted only when ρ≥η1; its damping increases by γ on rejection and decreases only on an accepted high-ratio boundary step, with floor λmin⁡.

[F2]

Uniformly large damping forces an acceptable LM trial in finitely many attempts: for every x∈S with J(x)Tr(x)≠0 and current λ≥λmin⁡, the stated finite cap reaches a feasible trial with positive predicted reduction and ρ≥η2, unless an earlier trial is accepted.

[F3]

A damped least-squares step solves a scaled trust-region model: the exact damped equation gives pred⁡=∥Jp∥22/2+λ∥Dp∥22.

[F4]

Continuously differentiable maps, local inverses, and local diffeomorphisms and The Jacobian matrix of partial derivatives and the gradient in the scalar-valued case give Dr=J; The chain rule for total derivatives: D(g∘f)(a)=Dg(f(a))∘Df(a) applies to Ψ(y)=∥y∥22/2 and r. The expansion Ψ(y+z)−Ψ(y)−⟨y,z⟩=∥z∥22/2=o(∥z∥2) gives DΨ(y)[z]=⟨y,z⟩.

[F7]

The Euclidean inner product ⟨x,y⟩=∑k<nxkyk on Rn: ∥v∥22=vTv for the standard Euclidean inner product.

[F8]

The matrix norm induced by a published vector p-norm: at p=2, the matrix norm is induced by the Euclidean vector norm.

[A1]

The sublevel set and bounded residual/Jacobian hypotheses in the Statement hold.

[A2]

Every accepted step has exact ratio at least η1 and the exact arithmetic run has no solver or objective-evaluation failure.

Proof

1.1givenA1A2F1F2F4F7

Let Ψ(y)=∥y∥22/2. The identity Ψ(y+z)−Ψ(y)−⟨y,z⟩=∥z∥22/2=o(∥z∥2) gives DΨ(y)[z]=⟨y,z⟩. Since r is C1 and J=Dr, the chain rule yields Dϕ(x)[h]=⟨r(x),J(x)h⟩=(J(x)Tr(x))Th, so ∇ϕ=JTr. At a nonstationary xk∈S, [F2] shows that the iteration either accepts an earlier trial or accepts once λ≥Λ, within N attempts. Thus no rejection-budget status occurs. The accepted actual reduction is ρ pred⁡≥η1pred⁡>0 by [F1], so ϕ(xk+1)<ϕ(xk)≤ϕ(x0) and xk+1∈S. Induction keeps all iterates in S.

2.1F1F2step 1.1algebra

The damping remains at least λmin⁡ by [F1]. If λk≥Λ, [F2] accepts the trial with ∥Dpk∥2≤Δ/2, so it is not a boundary step and the damping is unchanged. If λk<Λ, every rejection multiplies it by γ; the first value reaching Λ is less than γΛ, and [F2] accepts that interior trial without decreasing the damping. An earlier accepted step either preserves λk or decreases it to at least λmin⁡. Hence every damping used satisfies λmin⁡≤λk≤λmax⁡:=max⁡{λ0,γΛ}.

3.1A1F3F5F6F7F8step 2.1algebra

For an accepted nonstationary step, [F3] gives pred⁡≥λk∥Dpk∥22≥λmin⁡d2∥pk∥22. Its normal equation is JTr=−(JTJ+λkDTD)pk; [F6] gives ∥JT∥2=∥J∥2≤M, so submultiplicativity [F5] gives ∥JTr∥2≤(M2+λmax⁡d+2)∥pk∥2. Combining these inequalities yields pred⁡≥c∥JTr∥22 with the stated c>0.

4.1step 1.1step 2.1step 3.1A1F5F6F9algebra

Multiplying step 3.1 by ρ≥η1 proves the sufficient-decrease inequality in the Statement. Summing it over k=0,…,K−1 telescopes and uses ϕ(xK)≥0, giving η1c∑k<K∥J(xk)Tr(xk)∥22≤ϕ(x0). The nonnegative series is bounded, so its terms tend to zero. For y→x in U, [F5]–[F6] give ∥J(y)Tr(y)−J(x)Tr(x)∥2≤∥J(y)∥2∥r(y)−r(x)∥2+∥J(y)−J(x)∥2∥r(x)∥2→0 by [F9], since J(y) is locally bounded. Thus if a subsequence xkj converges to xˉ∈S⊂U, continuity gives J(xˉ)Tr(xˉ)=0.

5.1F4givenalgebra∎

To verify the limitation under the hypotheses, take U=R, r(t)=(t,1), and x0=0. Then ϕ(t)=(t2+1)/2, S={0}, S1=[−1,1], J=(1,0)T, and JTr(0)=0 while ∥r(0)∥2=1. On S1 take L=0, M=1, and R0=2; set D=1, d=d+=1, λmin⁡=λ0=1, γ=2, η1=1/4, η2=3/4, and Δ=1. These bounds give P=1/2, Λ=22, and finite cap N=3. The iteration returns stationary immediately, with nonzero residual. Thus even under the stated compact-sublevel and finite-attempt conditions the conclusion cannot be strengthened to r(xk)→0.

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