In mathematics, a metric projection is a function that maps each element of a metric space to the set of points nearest to that element in some fixed sub-space.[1] [2]
Formally, let X be a metric space with distance metric d, and let M be a fixed subset of X. Then the metric projection associated with M, denoted pM, is the following set-valued function from X to M:
Equivalently:pM(x)=\argminy\ind(x,y)
The elements in the setpM(x)=\{y\inM:d(x,y)\leqd(x,y')\forally'\inM\} =\{y\inM:d(x,y)=d(x,M)\}
\argminy\ind(x,y)
In general, pM is set-valued, as for every x, there may be many elements in M that have the same nearest distance to x. In the special case in which pM is single-valued, the set M is called a Chebyshev set. As an example, if (X,d) is a Euclidean space (Rn with the Euclidean distance), then a set M is a Chebyshev set if and only if it is closed and convex.[3]
If M is non-empty compact set, then the metric projection pM is upper semi-continuous, but might not be lower semi-continuous. But if X is a normed space and M is a finite-dimensional Chebyshev set, then pM is continuous.
Moreover, if X is a Hilbert space and M is closed and convex, then pM is Lipschitz continuous with Lipschitz constant 1.
Metric projections are used both to investigate theoretical questions in functional analysis and for practical approximation methods. They are also used in constrained optimization.[4]