A Fast Interior Point Method for Atomic Norm Soft Thresholding

03/02/2018
by   Thomas Lundgaard Hansen, et al.
0

The atomic norm provides a generalization of the ℓ_1-norm to continuous parameter spaces. When applied as a sparse regularizer for line spectral estimation the solution can be obtained by solving a convex optimization problem. This problem is known as atomic norm soft thresholding (AST). It can be cast as a semidefinite program and solved by standard methods. In the semidefinite formulation there are O(N^2) dual variables and a standard primal-dual interior point method requires at least O(N^6) flops per iteration. That has lead researcher to consider alternating direction method of multipliers (ADMM) for the solution of AST, but this method is still somewhat slow for large problem sizes. To obtain a faster algorithm we reformulate AST as a non-symmetric conic program. That has two properties of key importance to its numerical solution: the conic formulation has only O(N) dual variables and the Toeplitz structure inherent to AST is preserved. Based on it we derive FastAST which is a primal-dual interior point method for solving AST. Two variants are considered with the fastest one requiring only O(N^2) flops per iteration. Extensive numerical experiments demonstrate that FastAST solves AST significantly faster than a state-of-the-art solver based on ADMM.

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Code Repositories

fast-ast

FastAST - A fast primal-dual interior point method for line spectral estimation via atomic norm soft thresholding.


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