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LemmaStatement: AI-adaptedProof: AI-generatedPipeline-generatedprecheck passjudge pass (gpt-6-luna)
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A strictly separable finite sample has a unique hard-margin vector

Statement

For a finite strictly linearly separable sample in Rd, the hard-margin feasible set is nonempty and the problem of minimizing 12∥w∥22 over that set attains a unique minimizer.

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.

[F1]

Bias-free linear margins and the hard-margin SVM: the feasible constraints are the finitely many closed half-spaces mi(w)≥1, where each mi is a continuous linear functional.

[F2]

Proof

technique · direct existence by compactness and uniqueness by strict convexity
1.1givenF1algebra

Strict separability gives u with mi(u)>0 for every i. Since the sample is finite, a:=min⁡imi(u)>0. The scaled vector u/a satisfies mi(u/a)=mi(u)/a≥1, so the feasible set in [F1] is nonempty.

2.1F1F2step 1.1construct

Put R=∥u/a∥2 and K=F∩{w:∥w∥2≤R}. The set K is nonempty by step 1.1 and is closed and bounded by [F1], so [F2] makes it compact and gives a minimizer w∗ of 12∥w∥22 on K. Every feasible vector outside the radius-R ball has norm greater than R, while u/a∈K has norm R; therefore no such vector improves on w∗. Hence w∗ is a global minimizer on F.

3.1F1step 2.1algebra∎

If two distinct feasible minimizers w,z existed, their midpoint would remain feasible because each mi is linear. But ∥w+z2∥22=12∥w∥22+12∥z∥22−14∥w−z∥22 is strictly smaller than their common minimum value, a contradiction. Therefore the minimizer is unique.

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Sources