Decoupling Multivariate Polynomials Using First-Order Information

Abstract

We present a method to decompose a set of multivariate real polynomials into linear combinations of univariate polynomials in linear forms of the input variables. The method proceeds by collecting the first-order information of the polynomials in a set of operating points, which is captured by the Jacobian matrix evaluated at the operating points. The polyadic canonical decomposition of the three-way tensor of Jacobian matrices directly returns the unknown linear relations, as well as the necessary information to reconstruct the univariate polynomials. The conditions under which this decoupling procedure works are discussed, and the method is illustrated on several numerical examples.

References in corpus (2)

• Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
• Canonical polyadic decomposition of third-order tensors: reduction to generalized eigenvalue decomposition

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