Scalar product of machine-precision vectors:

Dot product of exact vectors:

Inner product of symbolic vectors:

The dot product of arbitrary-precision vectors:

Dot allows complex inputs, but does not conjugate any of them:

To compute the complex or Hermitian inner product, apply Conjugate to one of the inputs:

Some sources, particularly in the mathematical literature, conjugate the second argument:

Compute the norm of u using the two inner products:

Verify the result using Norm:

Dot product of sparse vectors:

Compute the scalar product of two QuantityArray vectors:

Define a rectangular matrix of dimensions :

Define a 2-vector and a 3-vector:

The matrix can be multiplied by the 2-vector only on the left:

Multiplying in the opposite order produces an error message due to the incompatible shapes:

Similarly, the matrix can be multiplied by the 3-vector only on the right:

Multiply the matrix on both sides at once:

Define a square matrix and a compatible vector:

The products m.v and v.m return different vectors:

The product v.m.v is a scalar:

Define a column and row matrices c and r with the same numerical entries as v:

Products involving m, c and r have the same entries as those involving m and v, but are all matrices:

Moreover, the products must be done in an order that respects the matrices' shapes:

Define a matrix and two vectors:

Since is a vector, is an allowed product:

Note that it is effectively multiplying on the left side of the matrix, not the right:

The product of a sparse matrix and sparse vector is a sparse vector:

Format the result as a row matrix:

The product of a sparse matrix and an ordinary vector is a normal vector:

The product of a structured matrix with a vector will retain the structure if possible:

The product of a normal matrix with a structured vector may have the structure of the vector:

Multiply real machine-number matrices:

Product of complex matrices:

Product of exact matrices:

Multiply in the other order:

Visualize the input and output matrices:

Multiply arbitrary-precision matrices:

Since , these matrices cannot be multiplied in the opposite order:

Product of symbolic matrices:

Product of finite field element matrices:

Product of CenteredInterval matrices:

Find random representatives mrep and nrep of m and n:

Verify that mn contains the product of mrep and nrep:

The product of sparse matrices is another sparse matrix:

Format the result:

The product of structured matrices preserves the structure if possible:

Format the result:

Cube a matrix:

Compare with MatrixPower:

Raise a matrix to the tenth power using Dot in combination with Apply (@@) and ConstantArray:

Verify the result:

Efficiently multiply large matrices:

Dot works for arrays of any rank:

The dimensions of the result are those of the input with the common dimension collapsed:

Any combination is allowed as long as products are done with a common dimension:

Create a rank-three array with three equal dimensions:

Create three vectors of the same dimension:

is the complete contraction that pairs with 's last level and with its first:

is the different contraction that pairs with 's first level and with its last:

Contract both levels of m with the second and third levels of a, respectively:

Dot of two sparse arrays is generally another sparse array:

Dot of a sparse array and an ordinary list may be another sparse array or an ordinary list:

Format the rank-three array:

The product of two SymmetrizedArray objects is generally another symmetrized array:

The symmetry of the new array may be much more complicated than the symmetry of either input:

Project the vector on the line spanned by the vector :

Visualize and its projection onto the line spanned by :

Project the vector on the plane spanned by the vectors and :

First, replace with a vector in the plane perpendicular to :

The projection in the plane is the sum of the projections onto and :

Find the component perpendicular to the plane:

Confirm the result by projecting onto the normal to the plane:

Visualize the plane, the vector and its parallel and perpendicular components:

Apply the Gram–Schmidt process to construct an orthonormal basis from the following vectors:

The first vector in the orthonormal basis, , is merely the normalized multiple :

For subsequent vectors, components parallel to earlier basis vectors are subtracted prior to normalization:

Confirm the answers using Orthogonalize:

Define a basis for :

Verify that the basis is orthonormal:

Find the components of a general vector with respect to this new basis:

Verify the components with respect to the :

Define a basis for :

Verify that it is a basis by showing that the matrix formed by the vectors has nonzero determinant:

The change of basis matrix is the inverse of the matrix whose columns are the :

A vector whose coordinates are in the standard bases will have coordinates with respect to :

Verify that these coordinates give back the vector :

The Frenet–Serret system encodes every space curve's properties in a vector basis and scalar functions. Consider the following curve:

Construct an orthonormal basis from the first three derivatives by subtracting parallel projections:

Ensure that the basis is right-handed:

Compute the curvature, , and torsion, , which quantify how the curve bends:

Verify the answers using FrenetSerretSystem:

Visualize the curve and the associated moving basis, also called a frame:

A matrix is orthogonal of . Show that a rotation matrix is orthogonal:

Confirm using OrthogonalMatrixQ:

A matrix is unitary of . Show that Pauli matrices are unitary:

Confirm with UnitaryMatrixQ:

A matrix is normal if . Show that the following matrix is normal:

Confirm using NormalMatrixQ:

Normal matrices include many other types of matrices as special cases. Unitary matrices are normal:

Hermitian or self-adjoint matrices for which are also normal, as the matrix shows:

However, the matrix is not a named type of normal matrix such as unitary or Hermitian:

In quantum mechanics, systems with finitely many states are represented by unit vectors and physical quantities by matrices that act on them. Consider a spin-1/2 particle such as an electron. It might be in a state such as the following:

The angular momentum in the direction is given by the following matrix:

The angular momentum of this state is :

The uncertainty in the angular momentum of this state is :

The uncertainty in the direction is computed analogously:

The uncertainty principle gives a lower bound on the product of uncertainties, :

Consider a linear mapping :

Get the matrix representation for :

Create a vector to be acted upon:

Apply the linear mapping to the vector using different methods:

Using with Dot is the faster method:

The application of a single matrix to multiple vectors can be computed as :

The matrix method is significantly faster than repeated application:

A real symmetric matrix gives a quadratic form by the formula :

Quadratic forms have the property that :

Equivalently, they define a homogeneous quadratic polynomial in the variables of :

The range of the polynomial can be , , or . In this case it is :

Visualize the polynomial:

A positive-definite, real symmetric matrix or metric defines an inner product by :

Being positive-definite means that the associated quadratic form is positive for :

Note that Dot itself is the inner product associated with the identity matrix:

Apply the Gram–Schmidt process to the standard basis to obtain an orthonormal basis:

Confirm that this basis is orthonormal with respect to the inner product :

An antisymmetric matrix for which defines a Hamiltonian 2-form :

Verify the conditions on :

is identically zero:

However, the form is nondegenerate, meaning implies :

Construct six vectors in dimension six:

Construct the totally antisymmetric array in dimension six using LeviCivitaTensor:

Compute the complete contraction :

This is equal to the determinant of the matrix formed by the vectors:

By the antisymmetry of , the reversed contraction differs by in dimension :