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Homogeneous Clarke flow produces approximate margin-KKT points
Statement
Let and let be locally Lipschitz functions that are positively homogeneous of common degree and admit the Clarke chain rule along absolutely continuous arcs. Let be a global Clarke gradient-flow trajectory for , and suppose . Put after . A limit point means that for every and , some satisfies . Then , and, writing , for every there is a feasible with that is an -approximate Clarke KKT point for minimizing subject to .
Statement references
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.
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 almost everywhere.
Homogeneous classifier margins and exponential training risk: , , and the normalized margins are and .
KKT conditions and MFCQ for margin constraints: an approximate KKT witness has nonnegative multipliers, margin subgradients, stationarity residual at most , and aggregate complementarity residual at most .
Smoothed margin monotonicity along homogeneous Clarke flow: after , 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 .
For a nonempty subset of with , 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.
Clarke chain rule for finite exponential empirical risk: for classifier-model sample margins, almost everywhere along the flow, for some .
Proof
The classifier restriction in [F7] is unnecessary for the following calculation, which we verify for the given margins. For , the smooth inverse and ordinary differentiation give by the gradient-limit characterization [F1]. The finite-sum limsup inequality bounds by the support function of , a nonempty compact convex set. If , choose its nearest point using [F6]. Convexity gives for , so the direction strictly separates from , contradicting that support bound. Hence . The flow inclusion therefore yields for some almost everywhere. Along the locally absolutely continuous flow, the assumed margin chain rules and ordinary scalar chain rule give almost everywhere.
For , put , , , , , , and . By [F5], , , and . Since , every and ; indeed . Homogeneity gives .
Local boundedness in [F1] and compactness of the unit sphere [F6] give a common bound for over all on that sphere, all , and . By the scaling identity in [F4], every therefore satisfies .
At almost every , choose the flow representation from [step 1.1], put , and define . Euler's identity [F4] gives , so and . Differentiating yields , since and . Also , because . Thus for , .
The normalized margin is and converges to by [F5]. Continuity of and the limit-point definition in the Statement imply . Fix . Choose an integer large enough that and . Then choose so that for every , , , and . By the limit-point property, choose with . Since [F4] and [step 1.1] give almost everywhere by step 3.1, absolute continuity makes strictly increasing; [F5] gives . Thus there is a unique with . Step 3.1 gives . Hence some nonexceptional has ; otherwise the integral would be larger. Integrating the direction bound in step 3.1 gives . Therefore is feasible and .
At this time , choose from [step 1.1], let , and put using [F4]. Set . Then and . For complementarity, set and ; then and , since for . Direct substitution gives . This is the -approximate KKT witness in [F3].
Depends on
- Clarke subdifferential and gradient-flow trajectory
- Homogeneous classifier margins and exponential training risk
- KKT conditions and MFCQ for margin constraints
- Euler identity for Clarke subgradients of homogeneous margins
- Clarke chain rule for finite exponential empirical risk
- Smoothed margin monotonicity along homogeneous Clarke flow
- For a nonempty subset of $\mathbb{R}^n$ with $n\ge1$, compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent
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Sources
- Kaifeng Lyu and Jian Li, Gradient Descent Maximizes the Margin of Homogeneous Neural Networks, Appendix C.3–C.4, Lemmas C.8–C.12 (standard reference, not scraped)
- Matus Telgarsky, Deep Learning Theory lecture notes, §§9.1 and 10.2 (standard reference, not scraped)