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The low-rank matrix approximation problem is ubiquitous in computationalmathematics. Traditionally, this problem is solved in spectral or Frobeniusnorms, where the accuracy of the approximation is related to the rate ofdecrease of the singular values of the matrix. However, recent results indicatethat this requirement is not necessary for other norms. In this paper, wepropose a method for solving the low-rank approximation problem in theChebyshev norm, which is capable of efficiently constructing accurateapproximations for matrices, whose singular values do not decrease or decreaseslowly.

On the algorithm of best approximation by low rank matrices in the Chebyshev norm