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  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
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Diagonal AdaGrad subproblem has a unique minimizer

Statement

Let C be a nonempty closed convex subset of R^d, let A=diag(a_1,...,a_d) with every a_j>0, and let eta>0 and x,g in R^d. Then the minimizer set

argmin⁡z∈C{η⟨g,z⟩+12∥z−x∥A2}

contains exactly one point.

Facts & Assumptions

Given: C,A,eta,x,g and the AdaGrad objective in the Statement.

[F1]

Diagonal AdaGrad for online convex losses: its update is the minimizer of eta<g,z>+||z-x||_A^2/2 over C, with A positive diagonal.

[F2]

Positive diagonal maps preserve closed convex sets: for d≥1, if T is diagonal with positive entries, TC is nonempty closed and convex whenever C is nonempty closed convex.

[F3]

Extended-real indicator of a set: the indicator of a set is zero on the set and +infinity off it.

[F4]

The proximal map of an indicator is Euclidean projection: the indicator of a nonempty closed convex set is proper, closed, and convex.

[F5]

Proper closed convex extended-real functions on Rn: proper means nonempty effective domain, convexity is the extended-real convexity inequality, and closedness means closed epigraph.

[F6]

Proximal mapping: prox_g(v) is the set of global minimizers of g(y)+||y-v||_2^2/2 when the parameter is one.

[F7]

Existence, resolvent characterization, and firm nonexpansiveness of the proximal map: for every proper closed convex g, prox_g(v) is a singleton.

[F9]

The Euclidean inner product ⟨x,y⟩=∑k<nxkyk on Rn: the Euclidean squared norm is the sum of coordinate squares.

[F10]

A convex subset of Rm contains every line segment between two of its points: convexity means every segment between points of C lies in C.

Proof

Given: The set, matrix, and objective in the Statement.

1.1F2F3F4F5F8F9F10givenconstructalgebra

If d=0, R0 contains only the empty vector by [F9], so nonempty C is that singleton and its sole point is the unique minimizer, with objective value zero. For the rest of the proof suppose d≥1. Let T=diag(sqrt(a_1),...,sqrt(a_d)). By [F8] each entry is the nonnegative square root of a_j; it is nonzero because a_j>0, so T is invertible and T^2=A. Set C'=TC. Since T is positive diagonal, [F2] makes C' nonempty, closed, and convex; [F10] supplies the convex-set convention used by [F2]. Therefore [F3]–[F5] make its indicator ι_{C'} a proper closed convex function.

2.1F1F3F6F9step 1.1givenalgebra

Put v=Tx−ηT−1g. Since T is diagonal, T(z−x) has squared norm ∥z−x∥A2 and ⟨T(z−x),T−1g⟩=⟨z−x,g⟩. Coordinate expansion gives η⟨g,z⟩+12∥z−x∥A2=12∥Tz−v∥22+η⟨g,x⟩−12η2∥T−1g∥22. The last two terms are independent of z, and z↦Tz bijects C with C′, so the minimizers correspond to prox⁡ιC′(v) by [F3] and [F6].

3.1F7step 2.1algebra∎

By [F7] the latter proximal set is a singleton. The inverse map T^{-1} is one-to-one, so its corresponding minimizer z in C is also unique. This proves the Statement.

Depends on

Used by

Cited to discharge well-definedness by Diagonal AdaGrad for online convex losses.

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Sources