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The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
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The largest coordinates attain the best s-term ℓ1 error
Statement
Let with and , and let use the one-based coordinate labels , with label corresponding to library coordinate . Order the coordinates by decreasing , breaking ties by increasing , and let be the first indices. For every , let agree with on and vanish on . Then for every with , Consequently , so the best -term tail error is attained by retaining the first coordinates in the specified order.
Statement references
Facts & Assumptions
Given: Integers , , a vector , its deterministic decreasing-magnitude order , the set , and an arbitrary with .
The -norms for rational , and : for , using the stated coordinate relabeling. In particular, for the zero-extended restriction , the summand is on and zero elsewhere.
Laws of finite sums and finite products, clauses 1, 3 and 4: finite sums are additive for two sequences on the same index range, split at a contiguous index cut, and preserve termwise inequalities; sums of nonnegative terms are nonnegative.
Proof
If , its retained mass is zero and is no larger than the nonnegative sum on . Otherwise write the ranks of the elements of in increasing order as , where . Then , so the specified ordering gives for each . The coordinate sum in [F2] may be reordered by successive adjacent swaps, each preserving the sum by associativity and commutativity of real addition; zero terms may be omitted. Thus , using termwise monotonicity and then the contiguous split at with a nonnegative remainder.
For every and , the zero-extended restrictions satisfy . Summing over the original full coordinate range and applying [F2] and the same-range additivity in [F3] gives . Taking and and subtracting the retained masses, step 1.1 implies . Applying this inequality to every admissible , while itself is admissible, shows that the finite minimum in [F1] equals .
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Sources
- Simon Foucart and Holger Rauhut, A Mathematical Introduction to Compressive Sensing, §2.1 Definition 2.2 (standard reference, not scraped)
- Shahar Mendelson, Holger Rauhut, and Rachel Ward, Improved Bounds for Sparse Recovery from Subsampled Random Convolutions, §2.1 (standard reference, not scraped)