# Dot

a.b.c or Dot[a,b,c]

gives products of vectors, matrices, and tensors.

# Details

• a.b gives an explicit result when a and b are lists with appropriate dimensions. It contracts the last index in a with the first index in b.
• Various applications of Dot:
•  {a1,a2}.{b1,b2} scalar product of vectors {a1,a2}.{{m11,m12},{m21,m22}} product of a vector and a matrix {{m11,m12},{m21,m22}}.{a1,a2} product of a matrix and a vector {{m11,m12},{m21,m22}}.{{n11,n12},{n21,n22}} product of two matrices
• The result of applying Dot to two tensors and is the tensor . Applying Dot to a rank tensor and a rank tensor gives a rank tensor. »
• Dot can be used on SparseArray and structured array objects. It will return an object of the same type as the input when possible. »
• Dot is linear in all arguments. » It does not define a complex (Hermitian) inner product on vectors. »
• When its arguments are not lists or sparse arrays, Dot remains unevaluated. It has the attribute Flat.

# Examples

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## Basic Examples(4)

Scalar product of vectors in three dimensions:

Scalar product of vectors in two dimensions:

Vectors are perpendicular if their inner product is zero:

Visualize the vectors:

The product of a matrix and a vector:

The product of a vector and a matrix:

The product of a matrix and two vectors:

The product of two matrices:

Multiply in the other order:

Use rectangular matrices:

## Scope(26)

### Dot Products of Vectors(7)

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:

### Matrix-Vector Multiplication(5)

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:

### Matrix-Matrix Multiplication(9)

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:

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:

### Higher-Rank Arrays(5)

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:

## Applications(16)

### Projections and Bases(6)

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 GramSchmidt 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 FrenetSerret 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:

### Matrices and Linear Operators(6)

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:

### Matrices and Arrays with Symmetry(4)

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 negative for :

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

Apply the GramSchmidt 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 :

## Properties & Relations(15)

Dot is linear in each argument:

For a vector with real entries, Norm[v] equals :

For a vector with complex values, the norm is given by :

For two vectors with real entries, , with the angle between and :

The scalar product of vectors is invariant under rotations:

For two matrices, the , entry of is the dot product of the row of with the column of :

Matrix multiplication is non-commutative, :

Use MatrixPower to compute repeated matrix products:

Compare with a direct computation:

The action of b on a vector is the same as acting four times with a on that vector:

For two tensors and , is the tensor :

Applying Dot to a rank- tensor and a rank- tensor gives a rank- tensor:

Dot with two arrays is a special case of Inner:

Dot can be implemented as a combination of TensorProduct and TensorContract:

Use Dot in combination with Flatten to contract multiple levels of one array with those of another:

TensorReduce can simplify expressions involving Dot:

Outer of two vectors can be computed with Dot:

Construct the column and row matrices corresponding to u and v:

The outer product equals c.r:

Dot of a row and column matrix equals the KroneckerProduct of the corresponding vectors:

## Possible Issues(2)

Dot effectively treats vectors multiplied from the right as column vectors:

Dot effectively treats vectors multiplied from the left as row vectors:

Dot does not give the standard inner product on :

Use Conjugate in one argument to get the Hermitian inner product:

Check that the result coincides with the square of the norm of a:

Wolfram Research (1988), Dot, Wolfram Language function, https://reference.wolfram.com/language/ref/Dot.html (updated 2012).

#### Text

Wolfram Research (1988), Dot, Wolfram Language function, https://reference.wolfram.com/language/ref/Dot.html (updated 2012).

#### BibTeX

@misc{reference.wolfram_2021_dot, author="Wolfram Research", title="{Dot}", year="2012", howpublished="\url{https://reference.wolfram.com/language/ref/Dot.html}", note=[Accessed: 23-September-2021 ]}

#### BibLaTeX

@online{reference.wolfram_2021_dot, organization={Wolfram Research}, title={Dot}, year={2012}, url={https://reference.wolfram.com/language/ref/Dot.html}, note=[Accessed: 23-September-2021 ]}

#### CMS

Wolfram Language. 1988. "Dot." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2012. https://reference.wolfram.com/language/ref/Dot.html.

#### APA

Wolfram Language. (1988). Dot. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/Dot.html