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A strictly separable finite sample has a unique hard-margin vector
Statement
For a finite strictly linearly separable sample in , the hard-margin feasible set is nonempty and the problem of minimizing over that set attains a unique minimizer.
Statement references
Facts & Assumptions
Given: A finite sample as in Bias-free linear margins and the hard-margin SVM, with a vector that strictly separates all of its examples.
Bias-free linear margins and the hard-margin SVM: the feasible constraints are the finitely many closed half-spaces , where each is a continuous linear functional.
For a nonempty subset of with , compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent: a nonempty closed and bounded subset of is compact, and a continuous real-valued function on it attains its minimum.
Proof
Strict separability gives with for every . Since the sample is finite, . The scaled vector satisfies , so the feasible set in [F1] is nonempty.
Put and . The set is nonempty by step 1.1 and is closed and bounded by [F1], so [F2] makes it compact and gives a minimizer of on . Every feasible vector outside the radius- ball has norm greater than , while has norm ; therefore no such vector improves on . Hence is a global minimizer on .
If two distinct feasible minimizers existed, their midpoint would remain feasible because each is linear. But is strictly smaller than their common minimum value, a contradiction. Therefore the minimizer is unique.
Depends on
Used by
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Sources
- Daniel Soudry et al., The Implicit Bias of Gradient Descent on Separable Data, equation (4) (standard reference, not scraped)
- Matus Telgarsky, Deep Learning Theory lecture notes, §10.1 (standard reference, not scraped)