Random Sampling of Sparse Trigonometric Polynomials

Abstract

We study the problem of reconstructing a multivariate trigonometric polynomial having only few non-zero coefficients from few random samples. Inspired by recent work of Candes, Romberg and Tao we propose to recover the polynomial by Basis Pursuit, i.e., by $\ell^1$-minimization. Numerical experiments show that in many cases the trigonometric polynomial can be recovered exactly provided the number $N$ of samples is high enough compared to the ``sparsity'' -- the number of non-vanishing coefficients. However, $N$ can be chosen small compared to the assumed maximal degree of the trigonometric polynomial. Hence, the proposed scheme may overcome the Nyquist rate. We present two theorems that explain this observation. Unexpectly, they establish a connection to an interesting combinatorial problem concerning set partitions, which seemingly has not yet been considered before.