368 research outputs found
Decoupling Multivariate Polynomials Using First-Order Information
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
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