Journal of Computational and Applied Mathematics · 2000 · 954 citations · 54 references
ComputingComputer ScienceMathematicsAlgorithmsQuantum ComputingScientific ComputingImage ProcessingLinear AlgebraMatrix ComputationMatrix FactorizationQuantum ComputationSignal ProcessingComputational Matrix TheoryFast Fourier TransformFourier TransformMatrix MultiplicationMatrix RepresentationsSemidefinite ProgrammingSparse Matrix Computation
The Kronecker product has a rich and very pleasing algebra that supports a wide range of fast, elegant, and practical algorithms. Several trends in scientific computing suggest that this important matrix operation will have an increasingly greater role to play in the future. First, the application areas where Kronecker products abound are all thriving. These include signal processing, image processing, semidefinite programming, and quantum computing. Second, sparse factorizations and Kronecker products are proving to be a very effective way to look at fast linear transforms. Researchers have taken the Kronecker methodology as developed for the fast Fourier transform and used it to build exciting alternatives. Third, as computers get more powerful, researchers are more willing to entertain problems of high dimension and this leads to Kronecker products whenever low-dimension techniques are “tensored” together.
54
Fundamentals of digital image processing
Computer Vision Graphics and Image Processing · 1989 · 4.7K citations
Lieven Vandenberghe, Stephen Boyd · SIAM Review · 1996 · 4.1K citations