Hadamard product (matrices)

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The Hadamard product operates on identically shaped matrices and produces a third matrix of the same dimensions.

In mathematics, the Hadamard product (also known as the Schur product[1] or the entrywise product[2]:ch. 5) is a binary operation that takes two matrices of the same dimensions and produces another matrix where each element ij is the product of elements ij of the original two matrices. It should not be confused with the more common matrix product. It is attributed to, and named after, either French mathematician Jacques Hadamard or German mathematician Issai Schur.

The Hadamard product is associative and distributive. Unlike the matrix product, it is also commutative.


For two matrices of the same dimension , the Hadamard product is a matrix of the same dimension as the operands, with elements given by

For matrices of different dimensions ( and , where or or both) the Hadamard product is undefined.


For example, the Hadamard product for a 3×3 matrix A with a 3×3 matrix B is


  • The Hadamard product is commutative, associative and distributive over addition. That is,
  • The identity matrix under Hadamard multiplication of two m-by-n matrices is an m-by-n matrix where all elements are equal to 1. This is different from the identity matrix under regular matrix multiplication, where only the elements of the main diagonal are equal to 1. Furthermore, a matrix has an inverse under Hadamard multiplication if and only if none of the elements are equal to zero.[3]
  • For vectors and , and corresponding diagonal matrices and with these vectors as their leading diagonals, the following identity holds:[2]:479

    where denotes the conjugate transpose of . In particular, using vectors of ones, this shows that the sum of all elements in the Hadamard product is the trace of . A related result for square and , is that the row-sums of their Hadamard product are the diagonal elements of :[4]

  • The Hadamard product is a principal submatrix of the Kronecker product.
  • The Hadamard product satisfies the rank inequality
  • If and are positive-definite matrices, then the following inequality involving the Hadamard product is valid:[5]
    where is the ith largest eigenvalue of .

Schur product theorem[edit]

The Hadamard product of two positive-semidefinite matrices is positive-semidefinite.[4] This is known as the Schur product theorem,[3] after German mathematician Issai Schur. For positive-semidefinite matrices A and B, it is also known that[4]

In programming languages[edit]

Hadamard multiplication is built into certain programming languages under various names. In MATLAB, GNU Octave, GAUSS and HP Prime, it is known as array multiplication, or in Julia broadcast multiplication, with the symbol .*.[6] In Fortran, R,[7] J and Wolfram Language (Mathematica), it is done through simple multiplication operator *, whereas the matrix product is done through the function matmul, %*%, +/ .* and the . operators, respectively. In Python with the NumPy numerical library or the SymPy symbolic library, multiplication of array objects as a1*a2 produces the Hadamard product, but otherwise multiplication as a1@a2 or matrix objects m1*m2 will produce a matrix product. The Eigen C++ library provides a cwiseProduct member function for the Matrix class (a.cwiseProduct(b)), while the Armadillo library uses the operator % to make compact expressions (a % b; a * b is a matrix product).


The Hadamard product appears in lossy compression algorithms such as JPEG. The decoding step involves an entry-for-entry product, i.e., Hadamard product.

Analogous operations[edit]

Other Hadamard operations are also seen in the mathematical literature,[8] namely the Hadamard root and Hadamard power (which are in effect the same thing because of fractional indices), defined for a matrix such that:


and for

The Hadamard inverse reads:[8]

A Hadamard division is defined as:[9][10]

See also[edit]


  1. ^ Davis, Chandler (1962). "The norm of the Schur product operation". Numerische Mathematik. 4 (1): 343–44. doi:10.1007/bf01386329.
  2. ^ a b Horn, Roger A.; Johnson, Charles R. (2012). Matrix analysis. Cambridge University Press.
  3. ^ a b Million, Elizabeth. "The Hadamard Product" (PDF). Retrieved 2 January 2012.
  4. ^ a b c Styan, George P. H. (1973), "Hadamard Products and Multivariate Statistical Analysis", Linear Algebra and its Applications, 6: 217–240, doi:10.1016/0024-3795(73)90023-2.
  5. ^ Hiai, Fumio; Lin, Minghua (February 2017). "On an eigenvalue inequality involving the Hadamard product". Linear Algebra and its Applications. 515: 313–320. doi:10.1016/j.laa.2016.11.017.
  6. ^ "Arithmetic Operators + - * / \ ^ ' -". MATLAB documentation. MathWorks. Retrieved 2 January 2012.
  7. ^ "Matrix multiplication". An Introduction to R. The R Project for Statistical Computing. 16 May 2013. Retrieved 24 August 2013.
  8. ^ a b Reams, Robert (1999). "Hadamard inverses, square roots and products of almost semidefinite matrices". Linear Algebra and its Applications. 288: 35–43. doi:10.1016/S0024-3795(98)10162-3.
  9. ^ Wetzstein, Gordon; Lanman, Douglas; Hirsch, Matthew; Raskar, Ramesh. "Supplementary Material: Tensor Displays: Compressive Light Field Synthesis using Multilayer Displays with Directional Backlighting" (PDF). MIT Media Lab.
  10. ^ Cyganek, Boguslaw (2013). Object Detection and Recognition in Digital Images: Theory and Practice. John Wiley & Sons. p. 109.