A fourth-order tensor relates two second-order tensors. Matrix notation of such relations is only possible, when the 9 components of the second-order tensor are . space equipped with coefficients taken from some good operator algebra. In this paper we introduce, using only the non-matricial language, both the classical (Grothendieck) projective tensor product of normed spaces. then the quotient vector space S/J may be endowed with a matricial ordering through .. By linear algebra, the restriction of σ to the algebraic tensor product is a.

Author: | Vozahn Kimi |

Country: | Armenia |

Language: | English (Spanish) |

Genre: | Travel |

Published (Last): | 8 November 2012 |

Pages: | 369 |

PDF File Size: | 16.52 Mb |

ePub File Size: | 12.11 Mb |

ISBN: | 924-4-40630-440-8 |

Downloads: | 40691 |

Price: | Free* [*Free Regsitration Required] |

Uploader: | Naramar |

## Matrix calculus

In analog with vector calculus this derivative is often written as the following. All of the work here can be done in this notation without use of the single-variable matrix notation. Also, Einstein notation can be very useful in proving the identities presented here see section on differentiation as an alternative to typical element notation, which can become cumbersome when the explicit sums are carried around.

As a first example, consider the gradient from vector calculus. In vector calculusthe derivative of a vector function y with respect to a vector x whose components represent a space is known as the pushforward or differentialor the Jacobian matrix. Not to be confused with geometric calculus or vector calculus. These are not as widely considered and a notation is not widely agreed upon.

### Matrix calculus – Wikipedia

It is important to realize the following:. To help make sense of all the identities below, keep in mind the most important rules: Match up the formulas below with those quoted in the source to determine the layout used for that particular type of derivative, but be careful not to assume that derivatives of other types necessarily follow the same kind of layout.

The three types of derivatives that have not been considered are those involving vectors-by-matrices, matrices-by-vectors, and matrices-by-matrices.

This is presented first because all of the operations that apply to vector-by-vector differentiation apply directly to vector-by-scalar or scalar-by-vector differentiation simply by reducing the appropriate vector in the numerator or denominator to a scalar.

As a result, the following layouts can often be found:. A single convention can be somewhat standard throughout a single field that commonly uses matrix calculus e. This book uses a mixed layout, i.

The Jacobian matrixaccording to Magnus and Neudecker, [2] is. There are, of course, a total of nine possibilities using scalars, vectors, and matrices. It is often easier to work in differential form and then convert back to normal derivatives. It has the advantage that one can easily manipulate arbitrarily matricia rank tensors, whereas tensors of rank higher than two are quite unwieldy with matrix notation. These are the derivative of a matrix by a scalar and the derivative of a scalar by a matrix.

As noted above, cases where vector and matrix denominators are written in transpose algebraa are equivalent to numerator layout with the denominators written without the transpose. Similarly we will find that the derivatives involving matrices will reduce to derivatives involving vectors in a corresponding way. Each of the previous two cases can be considered as an application u the derivative of a vector with respect to a vector, using a vector of size one appropriately.

That is, sometimes different conventions tensorila used in different contexts within the same book or paper. As for vectors, the other two types of higher matrix derivatives can be seen as applications of the derivative of a matrix by a matrix by using a matrix with one column in the correct place. Linear algebra and its applications 2nd ed. Note that exact equivalents of the scalar product rule and chain rule zlgebra not exist when applied to matrix-valued functions of matrices.

## Mathematics > Functional Analysis

Example Simple examples of this include the velocity vector in Euclidean spacewhich is the tangent vector of the position vector katricial as a function of time. By using this site, you agree to the Terms of Use and Privacy Policy. It is used in regression analysis to compute, for example, the ordinary least squares regression formula for the case of multiple explanatory variables.

An element of M 1,1 is a scalar, denoted with lowercase italic typeface: For each of the various tensoriial, we give numerator-layout and denominator-layout results, except in the cases above where denominator layout rarely occurs. The results of operations will be transposed when switching between numerator-layout and denominator-layout notation. Matrix theory Linear algebra Multivariable calculus. This leads to the following possibilities:.

Important examples of scalar functions of matrices include the trace of a matrix and the determinant.

For this reason, in this subsection we consider only how one can write the derivative of a matrix by another matrix. X T denotes matrix transposetr X is the traceand det X or X is the determinant.

### [math/] Tensor Products in Quantum Functional Analysis: the Non-Matricial Approach

A is not a function of x A is mafricial. This section discusses the similarities and differences between notational conventions that are used in the various fields that take advantage of matrix calculus.

In that case the scalar must be a function of each of the independent variables in the matrix. Matrix differential calculus with applications in g and econometrics Revised ed.

Moreover, we have used bold letters to indicate vectors and bold capital letters for matrices. To be consistent, we should do one of the following:. Views Read Edit View history.

Some authors use different conventions. A is not a function of xg X is any polynomial with scalar coefficients, or any matrix function defined by an infinite polynomial series e.