Footnote 4, p.10: , so forces
ProvedTimeVaryingGames.bregman_lower_boundgame-theorymirror-descentmonotone-gamesonline-learningp2o-gran-per-chapterp2o-plan-paperp2o-v1stochastic-approximation
Let be a distance-generating function with modulus on a compact convex set , with Bregman divergence , and let . Then:
- for every in the prox-domain ;
- every sequence with converges to .
Together with the reciprocity condition (RC), this shows that if and only if , which is how convergence of the energy is turned into convergence of the iterates.
Differences from the source:
- encoding: h : X → ℝ ∪ {∞} with dom h = C; ∂h(x) tested against all x′ ∈ X → h : E → ℝ with only its values on C used; the subgradient inequality tested for x′ ∈ C; reason: h = +∞ off C, so the inequality is automatic off C: the same subdifferential and prox-domain
- encoding: there exists a continuous selection ∇h of ∂h on dom ∂h → the selection is a parameter gradh with ContinuousOn and gradh x ∈ ∂h(x) on the prox-domain; reason: D and P are defined through the selection, so it must be fixed data
- encoding: K-strongly convex with modulus K (divided by in (A.5b), (40)) → 0 < Kc is part of IsDGF; reason: a strong-convexity modulus is positive; the paper divides by it
Preamble
import Mathlib import Definitions.Def_TimeVaryingGames_DGF open Filter Topology
Formal statement
namespace TimeVaryingGames
/-- **The Bregman divergence dominates the squared distance** (footnote 4).
arXiv:1809.03066v3, footnote 4, p.10: "Indeed, D(p, x_t) = h(p) − h(x_t) − ⟨∇h(x_t), p − x_t⟩
≥ (K/2)‖x_t − p‖², so x_t → p whenever D(p, x_t) → 0."
For a DGF `h` on the compact convex `C` with modulus `K` and a point `p ∈ C`:
1. `D(p, x) ≥ (K/2)‖x − p‖²` for every `x` in the prox-domain;
2. every sequence `x_t` in the prox-domain with `D(p, x_t) → 0` converges to `p`. -/
theorem bregman_lower_bound {E : Type*} [NormedAddCommGroup E] [NormedSpace ℝ E]
[FiniteDimensional ℝ E] {C : Set E} {Kc : ℝ} {h : E → ℝ} {gradh : E → E →L[ℝ] ℝ}
(hdgf : IsDGF C Kc h gradh) {p : E} (hp : p ∈ C) :
(∀ x ∈ proxDomain C h, Kc / 2 * ‖x - p‖ ^ 2 ≤ bregman h gradh p x) ∧
∀ xt : ℕ → E, (∀ t, xt t ∈ proxDomain C h) →
Tendsto (fun t => bregman h gradh p (xt t)) atTop (𝓝 0) → Tendsto xt atTop (𝓝 p) := by sorry
end TimeVaryingGames
Source
Duvocelle, Mertikopoulos, Staudigl, Vermeulen, Multi-Agent Online Learning in Time-Varying Games, arXiv:1809.03066v3, footnote 4, p.10
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.