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No-failure MMW rounds yield an approximate dual-feasible vector

Statement

Let n,m≥1 and consider a primal-dual pair with A1=In, b1=R>0, ∥Aj∥,∥C∥≤1, and a dual optimum attained by some vector of ℓ1 norm at most r≥1. Set w=r+1. Suppose for t=1,…,T the MMW update uses losses Mt=∑jyt,jAj−Cw, where yt≥0, ∥yt∥1≤r, b⋅yt≤α, and supplied estimates satisfy ∣a~t,j−tr⁡(Ajρt)∣≤θ, ∣c~t−tr⁡(Cρt)∣≤θ, and ∑jyt,ja~t,j−c~t≥−wθ. If 0<η≤1 and γ≥w(2θ+η+ln⁡(n)/(ηT)), then yˉ=γe1+T−1∑tyt is dual feasible and b⋅yˉ≤α+γR.

Facts & Assumptions

Given: The normalized finite SDP, each round's multiplier vector and trace estimates, and the MMW regret bound.

[F1]

Finite semidefinite primal-dual pair: dual feasibility requires nonnegative coordinates and positive semidefinite slack ∑jyjAj−C.

[F2]

Matrix multiplicative-weights game and update: the round density matrix is ρt and the round loss is tr⁡(Mtρt).

[F3]

Matrix multiplicative-weights regret bound: ∑ttr⁡(Mtρt)≤λmin⁡(∑tMt)+ηT+ln⁡(n)/η.

Proof

technique · direct
1.1givenF2algebra

By ∥Aj∥,∥C∥≤1 and ∥yt∥1≤r, the triangle inequality gives ∥Mt∥≤(r+1)/w=1, so the regret bound applies. The trace-estimate assumptions and ∑jyt,j≤r give ∣∑jyt,ja~t,j−c~t−tr⁡ ⁣((∑jyt,jAj−C)ρt)∣≤(r+1)θ=wθ. Together with the shifted-oracle inequality, this implies tr⁡(Mtρt)≥−2θ.

2.1givenF3step 1.1algebra

Sum the lower bounds from step 1.1 and combine them with [F3]. Writing S=∑tMt, we get −2θT≤λmin⁡(S)+ηT+ln⁡(n)/η, hence λmin⁡(S/T)≥−(2θ+η+ln⁡(n)/(ηT)).

3.1givenF1step 2.1algebra∎

The dual slack of yˉ is ∑jyˉjAj−C=γI+(w/T)S, because the identity coefficient contributes γA1=γI. Step 2.1 and the lower bound on γ make this matrix positive semidefinite. Also yˉ≥0 and b⋅yˉ=γR+T−1∑tb⋅yt≤γR+α. These are exactly the dual feasibility and objective claims in [F1].

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