University of Michigan, Ann Arbor, MI, USA · National University of Singapore, Singapore
Perturbing a deterministic
n-dimensional matrix with small Gaussian noise is a cornerstone of smoothed analysis of algorithms [Spielman and Teng, JACM 2004], as it reduces the condition number of the input to
O(n), and with it the complexity of many matrix algorithms. However, when deployed algorithmically, these perturbations are expensive due to the cost of generating and storing
n2 Gaussian random variables. We propose a perturbation that requires generating and storing
O(n) random numbers in
O(logn) bits of precision, and reduces the condition number of any deterministic matrix to
O(n), matching Gaussian perturbations. Our result in particular implies a better complexity for the perturbed conjugate gradient algorithm, showing that we can solve an
n×n linear system in linear space to within an arbitrarily small constant backward error using
O(n) matrix-vector products. In our construction, we introduce the concept of a pattern matrix, which is a dense deterministic matrix that maps all sparse vectors into dense vectors, and we combine it with a sparse perturbation whose entries are dependent and located in a non-uniform fashion. In order to analyze this construction, we develop new techniques for lower bounding the smallest singular value of a random matrix with dependent entries.