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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
contains exactly one point.
Facts & Assumptions
Given: C,A,eta,x,g and the AdaGrad objective in the Statement.
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.
Positive diagonal maps preserve closed convex sets: for , if T is diagonal with positive entries, TC is nonempty closed and convex whenever C is nonempty closed convex.
Extended-real indicator of a set: the indicator of a set is zero on the set and +infinity off it.
The proximal map of an indicator is Euclidean projection: the indicator of a nonempty closed convex set is proper, closed, and convex.
Proper closed convex extended-real functions on : proper means nonempty effective domain, convexity is the extended-real convexity inequality, and closedness means closed epigraph.
Proximal mapping: prox_g(v) is the set of global minimizers of g(y)+||y-v||_2^2/2 when the parameter is one.
Existence, resolvent characterization, and firm nonexpansiveness of the proximal map: for every proper closed convex g, prox_g(v) is a singleton.
Square roots exist: a unique with ; the positives are : each nonnegative real has a unique nonnegative square root.
The Euclidean inner product on : the Euclidean squared norm is the sum of coordinate squares.
A convex subset of 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.
If , contains only the empty vector by [F9], so nonempty is that singleton and its sole point is the unique minimizer, with objective value zero. For the rest of the proof suppose . 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.
Put . Since is diagonal, has squared norm and . Coordinate expansion gives . The last two terms are independent of , and bijects with , so the minimizers correspond to by [F3] and [F6].
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
- Diagonal AdaGrad for online convex losses
- Proximal mapping
- Existence, resolvent characterization, and firm nonexpansiveness of the proximal map
- Proper closed convex extended-real functions on $\mathbb R^n$
- Extended-real indicator of a set
- The proximal map of an indicator is Euclidean projection
- A convex subset of $\mathbb{R}^m$ contains every line segment between two of its points
- The Euclidean inner product $\langle x,y\rangle = \sum_{k<n} x_k y_k$ on $\mathbb{R}^n$
- Positive diagonal maps preserve closed convex sets
- Square roots exist: a unique $\sqrt{a} \ge 0$ with $(\sqrt{a})^2 = a$; the positives are $\{x^2 : x \neq 0\}$
Used by
Cited to discharge well-definedness by Diagonal AdaGrad for online convex losses.
Dependency tree · two levels
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Sources
- John Duchi, Elad Hazan, and Yoram Singer, Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, JMLR 12 (2011) (standard reference, not scraped)
- Sébastien Bubeck, Convex Optimization: Algorithms and Complexity, complete monograph (standard reference, not scraped)