finite-dimensional linear problem

Let $L:U\rightarrow V$ be a linear mapping, and let $v\in V$ be given. When both the domain $U$ and codomain $V$ are finite-dimensional, a linear equation $$L(u)=v,$$ where $u\in U$ is the unknown, can be solved by means of row reduction. To do so, we need to choose a basis $a_1,\ldots, a_m$ of the domain $U$ , and a basis $b_1,\ldots, b_n$ of the codomain $V$ . Let $M$ be the $n\times m$ transformation matrix of $L$ relative to these bases, and let $y\in\Rset^n$ be the coordinate vector of $v$ relative to the basis of $V$ . Expressing this in terms of matrix notation, we have

We can now restate the abstract linear equation as the matrix-vector equation$$ Mx =y$$ with $x\in \Rset^m$ unknown, or equivalently, as the following system of $n$ linear equations

with $x_1,\ldots, x_m$ unknown. Solutions $u\in U$ of the abstract linear equation $L(u)=v$ are in one-to-one correspondence with solutions of the matrix-vector equation $Mx=y$ . The correspondence is given by$$ u = \bmat{a_1,\ldots, a_m} \bmat{x_1 \\ \vdots \\ x_m}$$

Note that the dimension of the domain is the number of variables, while the dimension of the codomain is the number of equations. The equation is called under-determined or over-determined depending on whether the former is greater than the latter, or vice versa. In general, over-determined systems are inconsistent, while under-determined ones have multiple solutions. However, this is a ``rule of thumb'' only, and exceptions are not hard to find. A full understanding of consistency, and multiple solutions relies on the notions of kernel, image, rank, and is described by the rank-nullity theorem.

Remark.

Elementary applications focus exclusively on the coefficient matrix and the right-hand vector, and neglect to mention the underlying linear mapping. This is unfortunate, because the concept of a linear equation is much more general than the traditional notion of ``variables and equations'', and relies in an essential way on the idea of a linear mapping. See the example on polynomial as a case in point. Polynomial interpolation is a linear problem, but one that is specified abstractly, rather than in terms of variables and equations.