(1) For a function f : A→ B, we call A the domain, B the co-domain, f(A) the range, and f−1(b), for any b ∈ B, the fiber (or inverse image or pre-image) of b. For a subset S of B, f−1(B) = ⋃ b∈S f −1(b). (2) The sizes of fibers can be used to characterize injectivity (each fiber has size at most one), surjectivity (each fiber is non-empty), and bijectivity (each fiber has size exactly one). (3)...

One of the key problems in tensor completion is number uniformly random sample entries required for recovery guarantee. The main aim this paper to study n1×n2×n3 third-order based on transformed singular value decomposition, and provide a bound entries. Our approach make use multi-rank underlying instead its tubal rank bound. In numerical experiments synthetic imaging data sets, we demonstrate ...

This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The scheme provides an efficient way to deal with filters with large number of elements. It consists of a bank of full-rank adaptive filters that forms a transformation matrix and an adaptive reduced-rank filter that operates at the output of the bank of fil...