A bounded linear operator is compact when the closure of the image of its unit ball is compact. Equivalently every bounded sequence has a subsequence whose images converge; on a Banach target it also suffices that the image of its unit ball be totally bounded.
For a finite-rank operator that image is bounded in a finite-dimensional subspace, whose closed bounded sets are compact. Thus every finite-rank operator is compact.
Given a bounded sequence, first pass to a subsequence on which the images under converge. From that subsequence extract another on which the images under converge. Both image sequences, and hence their sum, converge on the latter subsequence. Thus is compact.
Extract a subsequence whose images converge, and apply continuity of the bounded operator to those images. This proves is compact. The reverse composition is compact as well, since sends a bounded sequence to a bounded sequence to which compactness of applies.
Suppose are compact and . For any take with . A finite -net of of the unit ball is then a finite -net of its image. That image is totally bounded, so is compact by the stated criterion. Hence is norm closed in .
Compact. Let keep the first coordinates. The operator has finite rank and , since the remaining diagonal coefficients are at most that number. Norm-closedness of compact operators proves the claim.
Not compact. For coordinate unit vectors with , the images are . Distinct such images have distance two, so no image subsequence converges.
Compact. This operator is , where is the compact diagonal operator in (a) and is the bounded backward shift in (b). The composition property applies. Equivalently its tails have the same vanishing-coefficient finite-rank approximation.
Compact. Its column indexed by has entries for , and zero otherwise. The sum of that column is for , by the elementary induction . For operators from the supremum of the absolute column sums is the operator norm: one inequality is the triangle inequality, and coordinate unit vectors give the reverse. Keeping just the first input columns is finite rank and its remainder norm is at most . Hence it converges to in norm, proving compactness.
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