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Two initial intervals converge to different minima of a periodic objective

Example

Let F(x)=1−cos⁡x on R, and use the fixed step α=1. Its gradient is F′(x)=sin⁡x, which is 1-Lipschitz, and F(x)≥0. Gradient descent is the iteration xt+1=xt−sin⁡xt. Every start x0∈(0,π) produces a sequence converging to the global minimizer 0, while every start x0∈(π,2π) produces a sequence converging to the distinct global minimizer 2π.

Facts & Assumptions

Given: The objective F(x)=1−cos⁡x, the fixed step α=1, and an initial point in either (0,π) or (π,2π).

[F1]

Gradient descent: with a fixed step tk, the update is xk+1=xk−tk∇F(xk).

[F2]

Gradient descent has vanishing gradients when the objective is bounded below along its iterates: for a continuously differentiable objective with L-Lipschitz gradient, fixed 0<α≤1/L, and an objective lower bounded along the iterates, the gradient norms tend to zero.

[F3]

The derivatives of sine and cosine are cosine and minus sine: (cos⁡x)′=−sin⁡x, sin⁡0=0, and cos⁡0=1.

[F5]

The derivative f′(c)=lim⁡x→cf(x)−f(c)x−c of f:A→R at a point c∈A that is a limit point of A, and differentiability on a set: f′(c) is the limit of (f(x)−f(c))/(x−c) as x→c through x≠c; a constant function has zero difference quotient.

[F6]

Sine and cosine are 1-Lipschitz on R: ∣sin⁡u−sin⁡v∣≤∣u−v∣ and ∣cos⁡u−cos⁡v∣≤∣u−v∣ for all real u,v.

[F7]

Pi is the first positive zero of sine: sin⁡x>0 for 0<x<π.

[F8]

The zero sets of sine and cosine and the least positive common period 2 pi: sin⁡x=0 exactly when x=mπ for an integer m, and both sine and cosine have period 2π.

[F9]

Parity and the Pythagorean identity for sine and cosine: sin⁡(−x)=−sin⁡x and ∣cos⁡x∣≤1.

[F10]

A monotone sequence converges if and only if it is bounded: every bounded monotone real sequence converges.

Verification

1.1givenF2F3F4F5F6F8F9algebra

By [F3, F4, F5], F′(x)=sin⁡x. Then [F6] makes the gradient continuous and 1-Lipschitz; [F9] gives F(x)≥0, and [F3, F8] give F(0)=F(2π)=0. Thus the hypotheses of [F2] hold with L=1 and α=1, so every trajectory has gradient norm tending to zero.

1.2givenF1F6F7F8F10algebrainduction

Suppose x0∈(0,π). If 0<xt≤x0, [F7] gives sin⁡xt>0, and [F6] with v=0 gives sin⁡xt≤xt; hence 0≤xt+1=xt−sin⁡xt<xt. If xt=0, it stays at 0. Induction shows 0≤xt+1≤xt≤x0, so [F10] gives a limit ℓ∈[0,x0]. Given ε>0, convergence gives an index after which both ∣xt−ℓ∣<ε/2 and ∣xt+1−ℓ∣<ε/2; hence ∣xt−xt+1∣→0 and, by the update, sin⁡xt→0. Also [F6] gives ∣sin⁡xt−sin⁡ℓ∣≤∣xt−ℓ∣→0, so sin⁡ℓ=0. By [F8], the only such zero in [0,x0] is 0, so ℓ=0.

2.1givenstep 1.2F1F6F7F8F9F10algebrainduction∎

Suppose x0∈(π,2π). If x0≤xt<2π, put ut=2π−xt∈(0,π). By [F7, F8, F9], sin⁡xt=−sin⁡ut<0, and [F6] gives −sin⁡xt=sin⁡ut≤ut=2π−xt. Therefore xt<xt+1=xt−sin⁡xt≤2π; if xt=2π, it remains there. Induction gives a nondecreasing sequence bounded above by 2π, so [F10] gives a limit ℓ∈[x0,2π]. Convergence implies ∣xt+1−xt∣→0 by the same two-term estimate used in step 1.2, so sin⁡ut→0. Since ut=2π−xt→2π−ℓ, [F6] gives ∣sin⁡ut−sin⁡(2π−ℓ)∣≤∣ut−(2π−ℓ)∣→0, hence sin⁡(2π−ℓ)=0. By [F8], the only such zero with 0≤2π−ℓ<2π−x0<π is 0. Thus ℓ=2π.

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