Block-Decoupling Multivariate Polynomials Using the Tensor Block-Term Decomposition
© Springer International Publishing Switzerland 2015. We present a tensor-based method to decompose a given set of multivariate functions into linear combinations of a set of multivariate functions of linear forms of the input variables. The method proceeds by forming a three-way array (tensor) by s...
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| creator | Dreesen, Philippe Goossens, T Ishteva, M De Lathauwer, Lieven Schoukens, Johan |
| description | © Springer International Publishing Switzerland 2015. We present a tensor-based method to decompose a given set of multivariate functions into linear combinations of a set of multivariate functions of linear forms of the input variables. The method proceeds by forming a three-way array (tensor) by stacking Jacobian matrix evaluations of the function behind each other. It is shown that a blockterm decomposition of this tensor provides the necessary information to block-decouple the given function into a set of functions with small input-output dimensionality. The method is validated on a numerical example. |
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We present a tensor-based method to decompose a given set of multivariate functions into linear combinations of a set of multivariate functions of linear forms of the input variables. The method proceeds by forming a three-way array (tensor) by stacking Jacobian matrix evaluations of the function behind each other. It is shown that a blockterm decomposition of this tensor provides the necessary information to block-decouple the given function into a set of functions with small input-output dimensionality. 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We present a tensor-based method to decompose a given set of multivariate functions into linear combinations of a set of multivariate functions of linear forms of the input variables. The method proceeds by forming a three-way array (tensor) by stacking Jacobian matrix evaluations of the function behind each other. It is shown that a blockterm decomposition of this tensor provides the necessary information to block-decouple the given function into a set of functions with small input-output dimensionality. The method is validated on a numerical example.</abstract><cop>Switzerland</cop><pub>Springer International Publishing</pub></addata></record> |
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| identifier | ISSN: 0302-9743 |
| ispartof | Latent Variable Analysis and Signal Separation, 2015, Vol.9237, p.14-21 |
| issn | 0302-9743 |
| language | eng |
| recordid | cdi_kuleuven_dspace_123456789_509163 |
| source | Lirias (KU Leuven Association); Springer Books |
| title | Block-Decoupling Multivariate Polynomials Using the Tensor Block-Term Decomposition |
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