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Cauchy-decrease trust-region iterations have liminf gradient norm zero
Statement
Let be , bounded below, and have an -Lipschitz gradient on every trial segment. At every iteration let be symmetric with for a fixed . Fix , , , and . Start from any 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 and its finite outer-cap return omitted. If this trajectory is infinite, then
Facts & Assumptions
Given: The smoothness, boundedness, and update assumptions of the Statement and the algorithm's Cauchy-decrease safeguard.
A finite-budget trust-region method with ratio-based radius updates: every selected step has predicted reduction at least times the Cauchy decrease; when , the algorithm uses the Cauchy step. A rejected trial multiplies the radius by ; an accepted trial either keeps it or increases it, capped by .
The Cauchy step gives a quantitative model decrease: for , with the zero-matrix convention stated there.
The actual-to-predicted ratio approaches one at small radii away from stationarity: if and then for a step with the stated Cauchy-decrease fraction. Zero denominators mean .
For the sequence is null, and for the sequence diverges to : if , then as .
Every subset of has a least upper bound and a greatest lower bound in , agreeing with the real supremum and infimum on nonempty sets bounded in : every tail has a greatest lower bound in ; it lies below every tail element and above every other lower bound.
Upper bound, least upper bound, and strict upper bound: if every element of a set is at most , then is an upper bound and its supremum is at most .
has a finite lower bound on .
Every is symmetric and obeys .
is -Lipschitz on each generated trial segment.
Proof
Given: The hypotheses in the Statement.
Fix any and suppose that for every . Put where zero denominators are interpreted as . Define . Every radius from index onward is at least : this holds at index ; an accepted update does not shrink the radius; and a rejected update can occur only when , so its new radius is greater than . The rejection restriction follows from [F1], [F3], and ; if then rejection is impossible.
Suppose, for contradiction, that for every after some index. An infinite block of rejections would give radii 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 using the zero-matrix convention if . Since acceptance means , each accepted step decreases by at least times this fixed positive quantity. Infinitely many such decreases contradict the lower bound [A1]. Therefore the gradients cannot eventually stay above any .
Put . Since every , zero is a lower bound of each tail; [F6] therefore gives for every , and [F5] gives . Suppose instead that . Choose strictly below this extended-real value (take if it is ). If for every , then [F7] makes an upper bound for all and therefore , a contradiction. Thus some , and [F6] implies for all . This contradicts step 2.1. Hence the liminf is zero.
Depends on
- A finite-budget trust-region method with ratio-based radius updates
- The actual-to-predicted ratio approaches one at small radii away from stationarity
- The Cauchy step gives a quantitative model decrease
- For $|r| < 1$ the sequence $r^k$ is null, and for $|r| > 1$ the sequence $|r|^k$ diverges to $+\infty$
- Limit superior and limit inferior of a real sequence as $\inf_n \sup_{k \ge n} x_k$ and $\sup_n \inf_{k \ge n} x_k$ in $\overline{\mathbb{R}}$
- Every subset of $\overline{\mathbb{R}}$ has a least upper bound and a greatest lower bound in $\overline{\mathbb{R}}$, agreeing with the real supremum and infimum on nonempty sets bounded in $\mathbb{R}$
- Upper bound, least upper bound, and strict upper bound
Used by
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Sources
- Jorge Nocedal and Stephen J. Wright, Numerical Optimization, second edition (standard reference, not scraped)
- Nick Gould, Continuous Optimization Algorithms, Part 3.2: Trust-Region Methods (standard reference, not scraped)