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Homogeneous Clarke flow produces approximate margin-KKT points

Statement

Let N,p≥1 and let q1,…,qN:Rp→R be locally Lipschitz functions that are positively homogeneous of common degree L>0 and admit the Clarke chain rule along absolutely continuous arcs. Let θ be a global Clarke gradient-flow trajectory for L(θ)=∑ie−qi(θ), and suppose L(θ(t0))<1. Put u(t)=θ(t)/∥θ(t)∥2 after t0. A limit point u∗ means that for every T≥t0 and ρ>0, some t>T satisfies ∥u(t)−u∗∥2<ρ. Then qmin⁡(u∗)>0, and, writing θ∗=u∗/qmin⁡(u∗)1/L, for every ε>0 there is a feasible θ^ with ∥θ^−θ∗∥2≤ε that is an (ε,ε)-approximate Clarke KKT point for minimizing 12∥θ∥22 subject to qi(θ)≥1.

Facts & Assumptions

Given: The finite positively homogeneous margins, their Clarke chain-rule property, a global flow, and a time when its exponential loss is below one.

[F1]

Clarke subdifferential and gradient-flow trajectory: Clarke subdifferentials are closed convex hulls of limiting ordinary gradients, are nonempty compact convex with the generalized directional derivative as support function, and are locally bounded with closed graph, and a global flow is locally absolutely continuous and satisfies θ˙∈−∂∘L(θ) almost everywhere.

[F2]

Homogeneous classifier margins and exponential training risk: qi(cθ)=cLqi(θ), L=∑ie−qi, and the normalized margins are qmin⁡(θ)/∥θ∥2L and −log⁡L(θ)/∥θ∥2L.

[F3]

KKT conditions and MFCQ for margin constraints: an approximate KKT witness has nonnegative multipliers, margin subgradients, stationarity residual at most ε, and aggregate complementarity residual at most δ.

[F4]

Euler identity for Clarke subgradients of homogeneous margins: ∂∘qi(cθ)=cL−1∂∘qi(θ) and ⟨θ,hi⟩=Lqi(θ).

[F5]

Smoothed margin monotonicity along homogeneous Clarke flow: after t0, the smoothed margin is nondecreasing, loss tends to zero, radius tends to infinity, and the smoothed and normalized margins converge to the same positive limit γ∞.

[F6]

For a nonempty subset of Rn with n≥1, compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent: nonempty closed bounded Euclidean sets are compact and continuous real-valued functions attain extrema on them; in particular, the unit sphere is compact.

[F7]

Clarke chain rule for finite exponential empirical risk: for classifier-model sample margins, almost everywhere along the flow, v:=θ˙=∑ie−qihi for some hi∈∂∘qi(θ).

Proof

technique · establish a radial-angle estimate, find one good time for each requested tolerance, and use the flow weights as approximate KKT multipliers
1.1F1F6F7givenconstructalgebra

The classifier restriction in [F7] is unnecessary for the following calculation, which we verify for the given margins. For fi=e−qi, the smooth inverse qi=−log⁡fi and ordinary differentiation give ∂∘fi(x)=−e−qi(x)∂∘qi(x) by the gradient-limit characterization [F1]. The finite-sum limsup inequality bounds L∘(x;w) by the support function of Cx:=∑i∂∘fi(x), a nonempty compact convex set. If g∈∂∘L(x)∖Cx, choose its nearest point c∈Cx using [F6]. Convexity gives ⟨g−c,b−c⟩≤0 for b∈Cx, so the direction g−c strictly separates g from Cx, contradicting that support bound. Hence ∂∘L(x)⊆Cx. The flow inclusion therefore yields θ˙=∑ie−qihi for some hi∈∂∘qi(θ) almost everywhere. Along the locally absolutely continuous flow, the assumed margin chain rules and ordinary scalar chain rule give L′=−⟨∑ie−qihi,θ˙⟩=−∥θ˙∥22 almost everywhere.

1.2F2F5algebra

For t≥t0, put r=∥θ∥2, u=θ/r, ϕ=L(θ), z=−log⁡ϕ, ai=e−qi(θ), pi=ai/ϕ, and μ=∑ipiqi(θ). By [F5], r>0, ϕ→0, and γ~(t)≥γ0:=γ~(t0)>0. Since ϕ<1, every qi>0 and μ≥z>0; indeed qi=z−log⁡pi. Homogeneity gives qmin⁡(u)=qmin⁡(θ)/rL=γˉ(t)≥γ0.

2.1F1F4F6step 1.2construct

Local boundedness in [F1] and compactness of the unit sphere [F6] give a common bound B for ∥h∥2 over all u on that sphere, all i, and h∈∂∘qi(u). By the scaling identity in [F4], every hi∈∂∘qi(θ) therefore satisfies ∥hi∥2≤BrL−1.

3.1F2F4F5step 1.1step 1.2step 2.1algebra

At almost every t≥t0, choose the flow representation from [step 1.1], put v=θ˙, and define β=⟨θ,v⟩/(r∥v∥2). Euler's identity [F4] gives ⟨θ,v⟩=Lϕμ>0, so v≠0 and 0<β≤1. Differentiating γ~=z/rL yields γ~′=L(μ/(β2rL)−z/rL)(log⁡r)′≥Lγ0(β−2−1)(log⁡r)′, since μ≥z and μ/rL≥γˉ≥γ0. Also ∥u′∥2≤∥v∥2/r≤BϕrL−2≤[B/(Lγ0)](log⁡r)′, because (log⁡r)′=Lϕμ/r2. Thus for s<t, Lγ0∫st(β−2−1)(log⁡r)′ dτ≤γ~(t)−γ~(s).

4.1F2F4F5F6step 1.1step 1.2step 3.1constructalgebra

The normalized margin is γˉ(t)=qmin⁡(u(t)) and converges to γ∞>0 by [F5]. Continuity of qmin⁡ and the limit-point definition in the Statement imply qmin⁡(u∗)=γ∞. Fix ε>0. Choose an integer k large enough that B/(Lγ0k)<εγ01/L/4 and 2/(kγ01/L)<ε. Then choose T so that for every t≥T, γ∞−γ~(t)≤Lγ0/k3, μ(t)≥z(t)>N/(eLγ02/Lε), and ∣γˉ(t)−1/L−γ∞−1/L∣<ε/2. By the limit-point property, choose s≥T with ∥u(s)−u∗∥2<εγ01/L/4. Since [F4] and [step 1.1] give (log⁡r)′=Lϕμ/r2>0 almost everywhere by step 3.1, absolute continuity makes r strictly increasing; [F5] gives r→∞. Thus there is a unique s′>s with log⁡r(s′)−log⁡r(s)=1/k. Step 3.1 gives Lγ0∫ss′(β−2−1)(log⁡r)′ dτ≤Lγ0/k3. Hence some nonexceptional t∈(s,s′) has β(t)−2−1≤1/k2; otherwise the integral would be larger. Integrating the direction bound in step 3.1 gives ∥u(t)−u∗∥2<εγ01/L/2. Therefore θ^:=u(t)/γˉ(t)1/L is feasible and ∥θ^−θ∗∥2<ε.

5.1F2F3F4F5step 1.1step 3.1step 4.1algebra∎

At this time t, choose hi from [step 1.1], let q∗=qmin⁡(θ(t)), and put h^i=hi/q∗(L−1)/L∈∂∘qi(θ^) using [F4]. Set λi=q∗(L−2)/Lrai/∥v∥2≥0. Then ∑iλih^i=q∗−1/Lr(v/∥v∥2) and ∥θ^−∑iλih^i∥2=2(1−β)/γˉ(t)1/L≤2/(kγ01/L)<ε. For complementarity, set di=qi(θ(t))−q∗≥0 and A=∑ie−di≥1; then pi=e−di/A and μ−q∗=∑ipidi≤N/e, since xe−x≤1/e for x≥0. Direct substitution gives ∑iλi(qi(θ^)−1)=βLγˉ(t)2/Lμ−q∗μ≤NeLγ02/Lμ<ε. This is the (ε,ε)-approximate KKT witness in [F3].

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