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Also known as Tikhonov regularization, named for Andrey Tikhonov, it is a method of regularization of ill-posed problems. [ a ] It is particularly useful to mitigate the problem of multicollinearity in linear regression , which commonly occurs in models with large numbers of parameters. [ 3 ]
Matrix multiplication shares some properties with usual multiplication. However, matrix multiplication is not defined if the number of columns of the first factor differs from the number of rows of the second factor, and it is non-commutative, [10] even when the product remains defined after changing the order of the factors. [11] [12]
The definition of matrix multiplication is that if C = AB for an n × m matrix A and an m × p matrix B, then C is an n × p matrix with entries = =. From this, a simple algorithm can be constructed which loops over the indices i from 1 through n and j from 1 through p, computing the above using a nested loop:
In probability theory and directional statistics, the von Mises distribution (also known as the circular normal distribution or the Tikhonov distribution) is a continuous probability distribution on the circle. It is a close approximation to the wrapped normal distribution, which is the circular analogue of the normal distribution.
In theoretical computer science, the computational complexity of matrix multiplication dictates how quickly the operation of matrix multiplication can be performed. Matrix multiplication algorithms are a central subroutine in theoretical and numerical algorithms for numerical linear algebra and optimization, so finding the fastest algorithm for matrix multiplication is of major practical ...
In the field of statistical learning theory, matrix regularization generalizes notions of vector regularization to cases where the object to be learned is a matrix. The purpose of regularization is to enforce conditions, for example sparsity or smoothness, that can produce stable predictive functions.
Andrey Nikolayevich Tikhonov (Russian: Андре́й Никола́евич Ти́хонов; 17 October 1906 – 7 October 1993) was a leading Soviet Russian mathematician and geophysicist known for important contributions to topology, functional analysis, mathematical physics, and ill-posed problems.
The matrix multiplication () yields the required square matrix and the matrix-vector product on the right hand side yields a vector of size . The result is a set of n {\displaystyle n} linear equations, which can be solved for δ {\displaystyle {\boldsymbol {\delta }}} .