A linear map is a function $f:V_{1}(F)→V_{2}(F)$ where $V_{1}(F)$ and $V_{2}(F)$ are two vector spaces over underlying fields $F$ such that:

$∀v_{1},v_{2}∈V_{1},c_{1},c_{2}∈Ff(c_{1}v_{1}+c_{2}v_{2})=c_{1}f(v_{1})+c_{2}f(v_{2})$

A common case is $F=R$, $V_{1}=R_{m}$ and $V_{2}=R_{n}$.

One thing that makes such functions particularly simple is that they can be fully specified by specifyin how they act on all possible combinations of input basis vectors: they are therefore specified by only a finite number of elements of $F$.

Every linear map in finite dimension can be represented by a matrix, the points of the domain being represented as vectors.

As such, when we say "linear map", we can think of a generalization of matrix multiplication that makes sense in infinite dimensional spaces like Hilbert spaces, since calling such infinite dimensional maps "matrices" is stretching it a bit, since we would need to specify infinitely many rows and columns.

The prototypical building block of infinite dimensional linear map is the derivative. In that case, the vectors being operated upon are functions, which cannot therefore be specified by a finite number of parameters, e.g.

For example, the left side of the time-independent Schrödinger equation is a linear map. And the time-independent Schrödinger equation can be seen as a eigenvalue problem.