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Exact safeguarded Levenberg-Marquardt iterates approach stationarity on compact sublevels
Statement
Let be on open , let , and fix . Suppose the initial sublevel is compact, its closed unit neighborhood lies in , and on the Jacobian is -Lipschitz with and . Fix nonsingular and constants such that and . Choose , , , , and , and define 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 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 , or every nonstationary iteration is accepted and where . Consequently , and every accumulation point is stationary. This does not imply ; 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.
Finite-budget Levenberg-Marquardt with a trust-ratio safeguard: a nonstationary exact LM trial is accepted only when ; its damping increases by on rejection and decreases only on an accepted high-ratio boundary step, with floor .
Uniformly large damping forces an acceptable LM trial in finitely many attempts: for every with and current , the stated finite cap reaches a feasible trial with positive predicted reduction and , unless an earlier trial is accepted.
A damped least-squares step solves a scaled trust-region model: the exact damped equation gives .
Continuously differentiable maps, local inverses, and local diffeomorphisms and The Jacobian matrix of partial derivatives and the gradient in the scalar-valued case give ; The chain rule for total derivatives: applies to and . The expansion gives .
Induced matrix norms are compatible with matrix-vector multiplication, submultiplicative, and satisfy ||I|| = 1: induced matrix norms bound products and matrix-vector products.
Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition and Spectral and Frobenius norms are unitarily invariant, are given by singular values, and satisfy the sharp rank comparison: a matrix and its transpose have the same singular values, hence the same Euclidean induced matrix norm.
The Euclidean inner product on : for the standard Euclidean inner product.
The matrix norm induced by a published vector p-norm: at , the matrix norm is induced by the Euclidean vector norm.
A map is continuous and its derivative matrix is continuous (Continuously differentiable maps, local inverses, and local diffeomorphisms, Total differentiability gives a local increment bound and therefore continuity).
The sublevel set and bounded residual/Jacobian hypotheses in the Statement hold.
Every accepted step has exact ratio at least and the exact arithmetic run has no solver or objective-evaluation failure.
Proof
Let . The identity gives . Since is and , the chain rule yields , so . At a nonstationary , [F2] shows that the iteration either accepts an earlier trial or accepts once , within attempts. Thus no rejection-budget status occurs. The accepted actual reduction is by [F1], so and . Induction keeps all iterates in .
The damping remains at least by [F1]. If , [F2] accepts the trial with , so it is not a boundary step and the damping is unchanged. If , every rejection multiplies it by ; the first value reaching is less than , and [F2] accepts that interior trial without decreasing the damping. An earlier accepted step either preserves or decreases it to at least . Hence every damping used satisfies .
For an accepted nonstationary step, [F3] gives . Its normal equation is ; [F6] gives , so submultiplicativity [F5] gives . Combining these inequalities yields with the stated .
Multiplying step 3.1 by proves the sufficient-decrease inequality in the Statement. Summing it over telescopes and uses , giving . The nonnegative series is bounded, so its terms tend to zero. For in , [F5]–[F6] give by [F9], since is locally bounded. Thus if a subsequence converges to , continuity gives .
To verify the limitation under the hypotheses, take , , and . Then , , , , and while . On take , , and ; set , , , , , , and . These bounds give , , and finite cap . 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 .
Depends on
- Finite-budget Levenberg-Marquardt with a trust-ratio safeguard
- The Euclidean inner product $\langle x,y\rangle = \sum_{k<n} x_k y_k$ on $\mathbb{R}^n$
- The matrix norm induced by a published vector p-norm
- Uniformly large damping forces an acceptable LM trial in finitely many attempts
- A damped least-squares step solves a scaled trust-region model
- Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition
- Spectral and Frobenius norms are unitarily invariant, are given by singular values, and satisfy the sharp rank comparison
- Induced matrix norms are compatible with matrix-vector multiplication, submultiplicative, and satisfy ||I|| = 1
- Continuously differentiable maps, local inverses, and local diffeomorphisms
- The Jacobian matrix of partial derivatives and the gradient in the scalar-valued case
- The chain rule for total derivatives: $D(g\circ f)(a)=Dg(f(a))\circ Df(a)$
- Total differentiability gives a local $O(\|h\|_2)$ increment bound and therefore continuity
Used by
Nothing in the library uses this result yet.
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Sources
- Jorge Nocedal and Stephen J. Wright, Numerical Optimization, second edition (standard reference, not scraped)
- David Bindel, CS 4220: Numerical Analysis, Gauss-Newton and nonlinear least squares (standard reference, not scraped)