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Cauchy-decrease trust-region iterations have liminf gradient norm zero

Statement

Let f:Rn→R be C1, bounded below, and have an L-Lipschitz gradient on every trial segment. At every iteration let Bk be symmetric with ∥Bk∥2≤MB for a fixed MB≥0. Fix 0<η1<η2<1, 0<γdec<1<γinc, 0<θC≤1, and 0<Δ0≤Δmax. Start from any x0∈Rn and consider an infinite-horizon trajectory that at each nonstationary iteration follows the model-formation, step-selection, ratio, and radius-update rules of A finite-budget trust-region method with ratio-based radius updates, with target 0 and its finite outer-cap return omitted. If this trajectory is infinite, then lim inf⁡k→∞∥∇f(xk)∥2=0.

Facts & Assumptions

Given: The smoothness, boundedness, and update assumptions of the Statement and the algorithm's Cauchy-decrease safeguard.

[F1]

A finite-budget trust-region method with ratio-based radius updates: every selected step has predicted reduction at least θC times the Cauchy decrease; when Δk≤∥gk∥2/(2MB), the algorithm uses the Cauchy step. A rejected trial multiplies the radius by γdec; an accepted trial either keeps it or increases it, capped by Δmax.

[F2]

The Cauchy step gives a quantitative model decrease: for gk≠0, mk(0)−mk(pC)≥12∥gk∥2min⁡{Δk,∥gk∥2/∥Bk∥2}, with the zero-matrix convention stated there.

[F3]

The actual-to-predicted ratio approaches one at small radii away from stationarity: if ∥gk∥2≥ε>0 and Δk≤min⁡{ε2MB,θC(1−η2)ε2(L+MB)}, then ρk≥η2 for a step with the stated Cauchy-decrease fraction. Zero denominators mean +∞.

[F5]

Limit superior and limit inferior of a real sequence as inf⁡nsup⁡k≥nxk and sup⁡ninf⁡k≥nxk in R‾: lim inf⁡kak=sup⁡nin, where in:=inf⁡{ak:k≥n}.

[F6]

Every subset of R‾ has a least upper bound and a greatest lower bound in R‾, agreeing with the real supremum and infimum on nonempty sets bounded in R: every tail has a greatest lower bound in R‾; it lies below every tail element and above every other lower bound.

[F7]

Upper bound, least upper bound, and strict upper bound: if every element of a set is at most b, then b is an upper bound and its supremum is at most b.

[A1]

f has a finite lower bound on Rn.

[A2]

Every Bk is symmetric and obeys ∥Bk∥2≤MB.

[A3]

∇f is L-Lipschitz on each generated trial segment.

Proof

Given: The hypotheses in the Statement.

1.1givenA2A3F1F3inductionalgebra

Fix any ε>0 and suppose that ∥gk∥2≥ε for every k≥K. Put dε:=min⁡{ε2MB,θC(1−η2)ε2(L+MB)},dˉε:=min⁡{dε,Δmax}, where zero denominators are interpreted as +∞. Define rε:=min⁡{ΔK,γdecdˉε}>0. Every radius from index K onward is at least rε: this holds at index K; an accepted update does not shrink the radius; and a rejected update can occur only when Δk>dˉε, so its new radius is greater than γdecdˉε≥rε. The rejection restriction follows from [F1], [F3], and ∥gk∥2≥ε; if dˉε=Δmax then rejection is impossible.

2.1givenA1A2F1F2F4step 1.1assume-contraalgebradischarge-contradiction

Suppose, for contradiction, that ∥gk∥2≥ε for every k after some index. An infinite block of rejections would give radii γdecjΔ tending to zero by [F4], contradicting step 1.1. Thus there are infinitely many accepted steps. At every such sufficiently late step, [F2] and [F1] give mk(0)−mk(pk)≥12θCεmin⁡{rε,ε/MB}>0, using the zero-matrix convention if MB=0. Since acceptance means ρk≥η1, each accepted step decreases f by at least η1 times this fixed positive quantity. Infinitely many such decreases contradict the lower bound [A1]. Therefore the gradients cannot eventually stay above any ε>0.

3.1step 2.1F5F6F7chooseassume-contradischarge-contradiction∎

Put ak:=∥gk∥2. Since every ak≥0, zero is a lower bound of each tail; [F6] therefore gives 0≤in≤ak for every k≥n, and [F5] gives lim inf⁡kak≥0. Suppose instead that lim inf⁡kak>0. Choose ε>0 strictly below this extended-real value (take ε=1 if it is +∞). If in≤ε for every n, then [F7] makes ε an upper bound for all in and therefore lim inf⁡kak=sup⁡nin≤ε, a contradiction. Thus some iN>ε, and [F6] implies ak≥iN>ε for all k≥N. This contradicts step 2.1. Hence the liminf is zero.

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