In mathematics, matrix calculus is a specialized notation for doing multivariable calculus, especially over spaces of matrices. It collects the various partial derivatives of a single function with respect to many variables, and/or of a multivariate function with respect to a single variable, into vectors and matrices that can be treated as single entities. This greatly simplifies operations such as finding the maximum or minimum of a multivariate function and solving systems of differential equations. The notation used here is commonly used in statistics and engineering, while the tensor index notation is preferred in physics. Two competing notational conventions split the field of matrix calculus into two separate groups. The two groups can be distinguished by whether they write the derivative of a scalar with respect to a vector as a column vector or a row vector. Both of these conventions are possible even when the common assumption is made that vectors should be treated as column vectors when combined with matrices (rather than row vectors). A single convention can be somewhat standard throughout a single field that commonly uses matrix calculus (e.g. econometrics, statistics, estimation theory and machine learning). However, even within a given field different authors can be found using competing conventions. Authors of both groups often write as though their specific conventions were standard. Serious mistakes can result when combining results from different authors without carefully verifying that compatible notations have been used. Definitions of these two conventions and comparisons between them are collected in the layout conventions section.
Scope Matrix calculus refers to a number of different notations that use matrices and vectors to collect the derivative of each component of the dependent variable with respect to each component of the independent variable. In general, the independent variable can be a scalar, a vector, or a matrix while the dependent variable can be any of these as well. Each different situation will lead to a different set of rules, or a separate calculus, using the broader sense of the term. Matrix notation serves as a convenient way to collect the many derivatives in an organized way. As a first example, consider the gradient from vector calculus. For a scalar function of three independent variables, f ( x 1 , x 2 , x 3 ) {\displaystyle f(x_{1},x_{2},x_{3})} , the gradient is given by the vector equation
∇ f = ∂ f ∂ x 1 x ^ 1 + ∂ f ∂ x 2 x ^ 2 + ∂ f ∂ x 3 x ^ 3 , {\displaystyle \nabla f={\frac {\partial f}{\partial x_{1}}}{\hat {x}}_{1}+{\frac {\partial f}{\partial x_{2}}}{\hat {x}}_{2}+{\frac {\partial f}{\partial x_{3}}}{\hat {x}}_{3},}
where x ^ i {\displaystyle {\hat {x}}_{i}} represents a unit vector in the x i {\displaystyle x_{i}} direction for 1 ≤ i ≤ 3 {\displaystyle 1\leq i\leq 3} . This type of generalized derivative can be seen as the derivative of a scalar, f, with respect to a vector, x {\displaystyle \mathbf {x} } , and its result can be easily collected in vector form.
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