This tag is for questions relating to "geometric functional analysis", lies at the interface of convex geometry, functional analysis and probability. It has numerous applications ranging from geometry of numbers and random matrices in pure mathematics to geometric tomography and signal processing in engineering and numerical optimization and learning theory in computer science.
Geometric functional analysis studies high dimensional linear structures. Some examples of such structures are Euclidean and Banach spaces, convex sets and linear operators in high dimensions. A central question of geometric functional analysis is: what do typical $~n$-dimensional structures look like when $~n~$ grows to infinity ? One of the main tools of geometric functional analysis is the theory of concentration of measure, which offers a geometric view on the limit theorems of probability theory. Geometric functional analysis thus bridges three areas – functional analysis, convex geometry and probability theory.
Applications: Ideas and methods of geometric functional analysis found a number of applications in computer science, especially in high-dimensional randomized algorithms. In many cases, when deterministic algorithms do not exist or are unknown, the methods relying on measure concentration provide efficient probabilistic substitutes. This is, however, a two-way street, as computer science questions have led to purely mathematical problems which are among the most important in the whole area.
References: