Euclidean subspace - Citizendia

Three one-dimensional subspaces (red, green and blue lines) of R². In Mathematics, the Cartesian coordinate system (also called rectangular coordinate system) is used to determine each point uniquely in a plane

In linear algebra, an Euclidean subspace (or subspace of Rⁿ) is a set of vectors that is closed under addition and scalar multiplication. Linear algebra is the branch of Mathematics concerned with In Mathematics, a vector space (or linear space) is a collection of objects (called vectors) that informally speaking may be scaled and added In Mathematics, a set is said to be closed under some operation if the operation on members of the set produces a member of the set Geometrically, a subspace is a flat in n-dimensional Euclidean space that passes through the origin. In Geometry, a flat is a subset of ''n''-dimensional space that is congruent to a Euclidean space of lower Dimension. Examples of subspaces include the solution set to a homogeneous system of linear equations, the subset of Euclidean space described by a system of homogeneous linear parametric equations, the span of a collection of vectors, and the null space, column space, and row space of a matrix. In Mathematics, a system of linear equations (or linear system) is a collection of Linear equations involving the same set of Variables For example In Mathematics, parametric equations are a method of defining a curve In the mathematical subfield of Linear algebra, the linear span, also called the linear hull, of a set of vectors in a Vector In Linear algebra, the column space of a matrix is the set of all possible Linear combinations of its column vectors In Linear algebra, the row space of a matrix is the set of all possible Linear combinations of its row vectors In Mathematics, a matrix (plural matrices) is a rectangular table of elements (or entries) which may be Numbers or more generally ^[1]

In abstract linear algebra, Euclidean subspaces are important examples of vector spaces. In Mathematics, a vector space (or linear space) is a collection of objects (called vectors) that informally speaking may be scaled and added In this context, a Euclidean subspace is simply a linear subspace of a Euclidean space. The concept of a linear subspace (or vector subspace) is important in Linear algebra and related fields of Mathematics.

Note on vectors and Rⁿ

In mathematics, Rⁿ denotes the set of all vectors with n real components:

$\textbf{R}^n = \left\{(x_1, x_2, \ldots, x_n) : x_1,x_2,\ldots,x_n \in \textbf{R} \right\}$ ^[2]

Here the word vector refers to any ordered list of numbers. In Mathematics, the real numbers may be described informally in several different ways Vectors can be written as either ordered tuples or as columns of numbers:

$(x_1, x_2, \ldots, x_n) = \left[\!\! \begin{array}{c} x_1 \\ x_2 \\ \vdots \\ x_n \end{array} \!\!\right]$ ^[3]

Geometrically, we regard vectors with n components as points in an n-dimensional space. In Geometry, Topology and related branches of mathematics a spatial point describes a specific point within a given space that consists of neither Volume That is, we identify the set Rⁿ with n-dimensional Euclidean space. Any subset of Rⁿ can be thought of as a geometric object (namely the object consisting of all the points in the subset). Using this mode of thought, a line in three-dimensional space is the same as the set of points on the line, and is therefore just a subset of R³.

Definition

A Euclidean subspace is a subset S of Rⁿ with the following properties:

The zero vector 0 is an element of S. In Linear algebra, the null vector or zero vector is the vector (0 0 &hellip 0 in Euclidean space, all of whose components are zero In Mathematics, the elements or members of a set (or more generally a class) are all those objects which when collected together make up the
If u and v are elements of S, then u + v is an element of S.
If v is an element of S and c is a scalar, then cv is an element of S. In Linear algebra, Real numbers are called Scalars and relate to vectors in a Vector space through the operation of Scalar multiplication

There are several common variations on these requirements, all of which are logically equivalent to the list above. ^[4] ^[5]

Because subspaces are closed under both addition and scalar multiplication, any linear combination of vectors from a subspace is again in the subspace. In Mathematics, a set is said to be closed under some operation if the operation on members of the set produces a member of the set In Mathematics, linear combinations are a concept central to Linear algebra and related fields of mathematics That is, if v₁, v₂, . . . , v_k are elements of a subspace S, and c₁, c₂, . . . , c_k are scalars, then

c₁ v₁ + c₂ v₂ + · · · + c_k v_k

is again an element of S. In Linear algebra, Real numbers are called Scalars and relate to vectors in a Vector space through the operation of Scalar multiplication

Geometric description

Three two-dimensional subspaces of R3. The center point is the zero vector.

Three two-dimensional subspaces of R³. The center point is the zero vector. In Linear algebra, the null vector or zero vector is the vector (0 0 &hellip 0 in Euclidean space, all of whose components are zero

Geometrically, a subspace of Rⁿ is simply a flat through the origin, i. In Geometry, a flat is a subset of ''n''-dimensional space that is congruent to a Euclidean space of lower Dimension. e. a copy of a lower dimensional (or equi-dimensional) Euclidean space sitting in n dimensions. For example, there are four different types of subspaces in R³:

The singleton set { (0, 0, 0) } is a zero-dimensional subspace of R³. In Mathematics, a singleton is a set with exactly one element
Any line through the origin is a one-dimensional subspace of R³.
Any plane through the origin is a two-dimensional subspace of R³.
The entire set R³ is a three-dimensional subspace of itself.

In n-dimensional space, there are subspaces of every dimension from 0 to n. In Mathematics, an n -dimensional space is a Topological space whose Dimension is n (where n is a fixed Natural

The geometric dimension of a subspace is the same as the number of vectors required for a basis (see below). Basis vector redirects here For basis vector in the context of crystals see Crystal structure.

Systems of linear equations

The solution set to any homogeneous system of linear equations with n variables is a subspace of Rⁿ:

$\left\{ \left[\!\! \begin{array}{c} x_1 \\ x_2 \\ \vdots \\ x_n \end{array} \!\!\right] \in \textbf{R}^n : \begin{alignat}{6} a_{11} x_1 &&\; + \;&& a_{12} x_2 &&\; + \cdots + \;&& a_{1n} x_n &&\; = 0& \\ a_{21} x_1 &&\; + \;&& a_{22} x_2 &&\; + \cdots + \;&& a_{2n} x_n &&\; = 0& \\ \vdots\;\;\; && && \vdots\;\;\; && && \vdots\;\;\; && \vdots\,& \\ a_{m1} x_1 &&\; + \;&& a_{m2} x_2 &&\; + \cdots + \;&& a_{mn} x_n &&\; = 0& \end{alignat} \right\}$

For example, the set of all vectors (x, y, z) satisfying the equations

$x + 3y + 2z = 0 \;\;\;\;\text{and}\;\;\;\; 2x - 4y + 5z = 0$

is a one-dimensional subspace of R³. In Mathematics, a system of linear equations (or linear system) is a collection of Linear equations involving the same set of Variables For example

Null space of a matrix

Main article: Null space

In linear algebra, a homogeneous system of linear equations can be written as a single matrix equation:

$A\textbf{x} = \textbf{0}$

The set of solutions to this equation is known as the null space of the matrix. In Mathematics, a matrix (plural matrices) is a rectangular table of elements (or entries) which may be Numbers or more generally For example, the subspace of R³ described above is the null space of the matrix

$A = \left[ \begin{alignat}{3} -1 && 5 && 2 &\\ 2 && \;\;-4 && \;\;\;\;3 &\end{alignat} \,\right]\text{.}$

Every subspace of Rⁿ can be described as the null space of some matrix (see algorithms, below).

Linear parametric equations

The subset of Rⁿ described by a system of homogeneous linear parametric equations is a subspace:

$\left\{ \left[\!\! \begin{array}{c} x_1 \\ x_2 \\ \vdots \\ x_n \end{array} \!\!\right] \in \textbf{R}^n : \begin{alignat}{7} x_1 &&\; = \;&& a_{11} t_1 &&\; + \;&& a_{12} t_2 &&\; + \cdots + \;&& a_{1m} t_m & \\ x_2 &&\; = \;&& a_{21} t_1 &&\; + \;&& a_{22} t_2 &&\; + \cdots + \;&& a_{2m} t_m & \\ \vdots \,&& && \vdots\;\;\; && && \vdots\;\;\; && && \vdots\;\;\; & \\ x_n &&\; = \;&& a_{n1} t_1 &&\; + \;&& a_{n2} t_2 &&\; + \cdots + \;&& a_{nm} t_m & \\ \end{alignat} \text{ for some } t_1,\ldots,t_m\in\textbf{R} \right\}$

For example, the set of all vectors (x, y, z) parameterized by the equations

$x = 2t_1 + 3t_2,\;\;\;\;y = 5t_1 - 4t_2,\;\;\;\;\text{and}\;\;\;\;z = -t_1 + 2t_2$

is a two-dimensional subspace of R³. In Mathematics, parametric equations are a method of defining a curve

Span of vectors

Main article: Linear span

In linear algebra, the system of parametric equations can be written as a single vector equation:

$\left[ \begin{alignat}{1} x& \\ y& \\ z& \end{alignat}\,\right] \;=\; t_1 \!\left[ \begin{alignat}{1} 2& \\ 5& \\ -1& \end{alignat}\,\right] + t_2 \!\left[ \begin{alignat}{1} 3& \\ -4& \\ 2& \end{alignat}\,\right]$

The expression on the right is called a linear combination of the vectors (2, 5, −1) and (3, −4, 2). In the mathematical subfield of Linear algebra, the linear span, also called the linear hull, of a set of vectors in a Vector In Mathematics, linear combinations are a concept central to Linear algebra and related fields of mathematics These two vectors are said to span the resulting subspace.

In general, a linear combination of vectors v₁, v₂, . . . , v_k is any vector of the form

$t_1 \textbf{v}_1 + \cdots + t_k \textbf{v}_k\text{.}$

The set of all possible linear combinations is called the span:

$\text{Span} \{ \textbf{v}_1, \ldots, \textbf{v}_k \} = \left\{ t_1 \textbf{v}_1 + \cdots + t_k \textbf{v}_k : t_1,\ldots,t_k\in\mathbf{R} \right\}$

If the vectors v₁,. . . ,v_k have n components, then their span is a subspace of Rⁿ. Geometrically, the span is the flat through the origin in n-dimensional space determined by the points v₁,. . . ,v_k.

Example

The xz-plane in R³ can be parameterized by the equations

$x = t_1, \;\;\; y = 0, \;\;\; z = t_2$

As a subspace, the xz-plane is spanned by the vectors (1, 0, 0) and (0, 0, 1). Every vector in the xz-plane can be written as a linear combination of these two:

$(t_1, 0, t_2) = t_1(1,0,0) + t_2(0,0,1)\text{.}\,$

Geometrically, this corresponds to the fact that every point on the xz-plane can be reached from the origin by first moving some distance in the direction of (1, 0, 0) and then moving some distance in the direction of (0, 0, 1).

Column space and row space

Main articles: Column space and Row space

A system of linear parametric equations can also be written as a single matrix equation:

$\textbf{x} = A\textbf{t}\;\;\;\;\text{where}\;\;\;\;A = \left[ \begin{alignat}{2} 2 && 3 & \\ 5 && \;\;-4 & \\ -1 && 2 & \end{alignat} \,\right]\text{.}$

In this case, the subspace consists of all possible values of the vector x. In Linear algebra, the column space of a matrix is the set of all possible Linear combinations of its column vectors In Linear algebra, the row space of a matrix is the set of all possible Linear combinations of its row vectors In linear algebra, this subspace is known as the column space (or image) of the matrix A. In Mathematics, the image of a preimage under a given function is the set of all possible function outputs when taking each element of the preimage It is precisely the subspace of Rⁿ spanned by the column vectors of A.

The row space of a matrix is the subspace spanned by its row vectors. The row space is interesting because it is the orthogonal complement of the null space (see below). In the mathematical fields of Linear algebra and Functional analysis, the orthogonal complement W^\bot of a subspace W

Independence, basis, and dimension

Main articles: Linear independence, Basis (linear algebra), and Dimension (vector space)

The vectors u and v are a basis for this two-dimensional subspace of R³. In Linear algebra, a family of vectors is linearly independent if none of them can be written as a Linear combination of finitely many other vectors Basis vector redirects here For basis vector in the context of crystals see Crystal structure. In Mathematics, the dimension of a Vector space V is the cardinality (i

In general, a subspace of Rⁿ determined by k parameters (or spanned by k vectors) has dimension k. However, there are exceptions to this rule. For example, the subspace of R³ spanned by the three vectors (1, 0, 0), (0, 0, 1), and (2, 0, 3) is just the xz-plane, with each point on the plane described by infinitely many different values of t₁, t₂, t₃.

In general, vectors v₁,. . . ,v_k are called linearly independent if

$t_1 \textbf{v}_1 + \cdots + t_k \textbf{v}_k \;\ne\; u_1 \textbf{v}_1 + \cdots + u_k \textbf{v}_k$

for (t₁, t₂, . . . , t_k) ≠ (u₁, u₂, . . . , u_k). ^[6] If v₁, . . . , v_k are linearly independent, then the coordinates t₁, . . . , t_k for a vector in the span are uniquely determined.

A basis for a subspace S is a set of linearly independent vectors whose span is S. The number of elements in a basis is always equal to the geometric dimension of the subspace. Any spanning set for a subspace can be changed into a basis by removing redundant vectors (see algorithms, below).

Example

Let S be the subspace of R⁴ defined by the equations

$x_1 = 2 x_2\;\;\;\;\text{and}\;\;\;\;x_3 = 5x_4$

Then the vectors (2, 1, 0, 0) and (0, 0, 5, 1) are a basis for S. In particular, every vector that satisfies the above equations can be written uniquely as a linear combination of the two basis vectors:

$(2t_1, t_1, 5t_2, t_2) = t_1(2, 1, 0, 0) + t_2(0, 0, 5, 1)\,$

The subspace S is two-dimensional. Geometrically, it is the plane in R⁴ passing through the points (0, 0, 0, 0), (2, 1, 0, 0), and (0, 0, 5, 1).

Algorithms

Most algorithms for dealing with subspaces involve row reduction. In Linear algebra, Gaussian elimination is an efficient Algorithm for solving systems of linear equations, to find the rank of a matrix This is the process of applying elementary row operations to a matrix until it reaches either row echelon form or reduced row echelon form. In Mathematics, an elementary matrix is a simple matrix which differs from the Identity matrix in a minimal way In Linear algebra a matrix is in row echelon form if All nonzero rows are above any rows of all zeroes and The Leading coefficient In Linear algebra a matrix is in row echelon form if All nonzero rows are above any rows of all zeroes and The Leading coefficient Row reduction has the following important properties:

The reduced matrix has the same null space as the original.
Row reduction does not change the span of the row vectors, i. e. the reduced matrix has the same row space as the original.
Row reduction does not affect the linear dependence of the column vectors.

Basis for a row space

Input An m × n matrix A.

Output A basis for the row space of A. In Linear algebra, the row space of a matrix is the set of all possible Linear combinations of its row vectors

Use elementary row operations to put A into row echelon form.
The nonzero rows of the echelon form are a basis for the row space of A.

See the article on row space for an example. In Linear algebra, the row space of a matrix is the set of all possible Linear combinations of its row vectors In Linear algebra, the row space of a matrix is the set of all possible Linear combinations of its row vectors

If we instead put the matrix A into reduced row echelon form, then the resulting basis for the row space is uniquely determined. This provides an algorithm for checking whether two row spaces are equal and, by extension, whether two subspaces of Rⁿ are equal.

Subspace membership

Input A basis {b₁, b₂, . . . , b_k} for a subspace S of Rⁿ, and a vector v with n components.

Output Determines whether v is an element of S

Create a (k + 1) × n matrix A whose rows are the vectors b₁,. . . ,b_k and v.
Use elementary row operations to put A into row echelon form.
If the echelon form has a row of zeroes, then the vectors {b₁, . . . , b_k, v} are linearly dependent, and therefore v ∈ S .

Basis for a column space

Input An m × n matrix A

Output A basis for the column space of A

Use elementary row operations to put A into row echelon form. In Linear algebra, the column space of a matrix is the set of all possible Linear combinations of its column vectors
Determine which columns of the echelon form have pivots. In Linear algebra a matrix is in row echelon form if All nonzero rows are above any rows of all zeroes and The Leading coefficient The corresponding columns of the original matrix are a basis for the column space.

See the article on column space for an example. In Linear algebra, the column space of a matrix is the set of all possible Linear combinations of its column vectors In Linear algebra, the column space of a matrix is the set of all possible Linear combinations of its column vectors

This produces a basis for the column space that is a subset of the original column vectors. It works because the columns with pivots are a basis for the column space of the echelon form, and row reduction does not change the linear dependence relationships between the columns.

Coordinates for a vector

Input A basis {b₁, b₂, . . . , b_k} for a subspace S of Rⁿ, and a vector v ∈ S

Output Numbers t₁, t₂, . . . , t_k such that v = t₁b₁ + ··· + t_kb_k

Create an augmented matrix A whose columns are b₁,. In Linear algebra, the augmented matrix of a matrix is obtained by combining two matrices . . ,b_k , with the last column being v.
Use elementary row operations to put A into reduced row echelon form.
Express the final column of the reduced echelon form as a linear combination of the first k columns. The coefficients used are the desired numbers t₁, t₂, . . . , t_k. (These should be precisely the first k entries in the final column of the reduced echelon form. )

If the final column of the reduced row echelon form contains a pivot, then the input vector v does not lie in S.

Basis for a null space

Input An m × n matrix A.

Output A basis for the null space of A

Use elementary row operations to put A in reduced row echelon form.
Using the reduced row echelon form, determine which of the variables x₁, x₂, . . . , x_n are free. Write equations for the dependent variables in terms of the free variables.
For each free variable x_i, choose a vector in the null space for which x_i = 1 and the remaining free variables are zero. The resulting collection of vectors is a basis for the null space of A.

See the article on null space for an example. In Linear algebra, the kernel or null space (also nullspace) of a matrix A is the set of all vectors x for which

Equations for a subspace

Input A basis {b₁, b₂, . . . , b_k} for a subspace S of Rⁿ

Output An (n − k) × n matrix whose null space is S.

Create a matrix A whose rows are b₁, b₂, . . . , b_k.
Use elementary row operations to put A into reduced row echelon form.
Let c₁, c₂, . . . , c_n be the columns of the reduced row echelon form. For each column without a pivot, write an equation expressing the column as a linear combination of the columns with pivots.
This results in a homogeneous system of n − k linear equations involving the variables c₁,. . . ,c_n. The (n − k) × n matrix corresponding to this system is the desired matrix with nullspace S.

Example: If the reduced row echelon form of A is

$\left[ \begin{alignat}{6} 1 && 0 && -3 && 0 && 2 && 0 \\ 0 && 1 && 5 && 0 && -1 && 4 \\ 0 && 0 && 0 && 1 && 7 && -9 \\ 0 && \;\;\;\;\;0 && \;\;\;\;\;0 && \;\;\;\;\;0 && \;\;\;\;\;0 && \;\;\;\;\;0 \end{alignat} \,\right]$

then the column vectors c₁, . . . , c₆ satisfy the equations

$\begin{alignat}{1} \textbf{c}_3 &= -3\textbf{c}_1 + 5\textbf{c}_2 \\ \textbf{c}_5 &= 2\textbf{c}_1 - \textbf{c}_2 + 7\textbf{c}_3 \\ \textbf{c}_6 &= 4\textbf{c}_2 - 9\textbf{c}_3 \end{alignat}\text{.}$

It follows that the row vectors of A satisfy the equations

$\begin{alignat}{1} x_3 &= -3x_1 + 5x_2 \\ x_5 &= 2x_1 - x_2 + 7x_3 \\ x_6 &= 4x_2 - 9x_3 \end{alignat}\text{.}$

In particular, the row vectors of A are a basis for the null space of the corresponding matrix.

Operations on subspaces

In R³, the intersection of two-dimensional subspaces is one-dimensional.

Intersection

If U and V are subspaces of Rⁿ, their intersection is also a subspace:

$U \cap V = \left\{ \textbf{x}\in\textbf{R}^n : \textbf{x}\in U\text{ and }\textbf{x}\in V \right\}$

The dimension of the intersection satisfies the inequality

$\dim(U) + \dim(V) - n \leq \dim(U \cap V) \leq \min(\dim U,\,\dim V)\text{.}$

The minimum is the most general case^[7], and the maximum only occurs when one subspace is contained in the other. In Mathematics, the intersection of two sets A and B is the set that contains all elements of A that also belong to B (or equivalently For example, the intersection of two-dimensional subspaces in R³ has dimension one or two (with two only possible if they are the same plane). The intersection of three-dimensional subspaces in R⁵ has dimension one, two, or three, with most pairs intersecting along a line.

The codimension of a subspace U in Rⁿ is the difference n − dim(U). In Mathematics, codimension is a basic geometric idea that applies to Subspaces in Vector spaces and more generally to Submanifolds in Manifolds Using codimension, the inequality above can be written

$\max(\text{codim } U,\,\text{codim } V) \leq \text{codim}(U \cap V) \leq \text{codim}(U) + \text{codim}(V) \text{.}$

Sum

If U and V are subspaces of Rⁿ, their sum is the subspace

$U + V = \left\{ \textbf{u} + \textbf{v} : \textbf{u}\in U\text{ and }\textbf{v}\in V \right\}\text{.}$

For example, the sum of two lines is the plane that contains them both. The dimension of the sum satisfies the inequality

$\max(\dim U,\dim V) \leq \dim(U + V) \leq \dim(U) + \dim(V)\text{.}$

Here the minimum only occurs if one subspace is contained in the other, while the maximum is the most general case. ^[8] The dimension of the intersection and the sum are related:

$\dim(U+V) = \dim(U) + \dim(V) - \dim(U \cap V)$

Orthogonal complement

Main article: Orthogonal complement

The orthogonal complement of a subspace U is the subspace

$U^\bot = \left\{\textbf{x}\in\textbf{R}^n : \textbf{x} \cdot \textbf{u}=0\text{ for every }\textbf{u}\in U \right\}$

Here x · u denotes the dot product of x and u. In the mathematical fields of Linear algebra and Functional analysis, the orthogonal complement W^\bot of a subspace W In Mathematics, the dot product, also known as the scalar product, is an operation which takes two vectors over the Real numbers R For example, if U is a plane through the origin in R³, then U^⊥ is the line perpendicular to this plane at the origin.

If b₁, b₂, . . . , b_k is a basis for U, then a vector x is in the orthogonal complement of U if and only if it is orthogonal to each b_i. In Mathematics, two Vectors are orthogonal if they are Perpendicular, i It follows that the null space of a matrix is the orthogonal complement of the row space.

The dimension of a subspace and its orthogonal complement are related by the equation

$\dim(U) + \dim(U^\bot) = n$

That is, the dimension of U^⊥ is equal to the codimension of U. In Mathematics, codimension is a basic geometric idea that applies to Subspaces in Vector spaces and more generally to Submanifolds in Manifolds The intersection of U and U^⊥ is the origin, and the sum of U and U^⊥ is all of Rⁿ

Orthogonal complements satisfy a version of De Morgan's laws:

$(U + V)^\bot = U^\bot \cap V^\bot\;\;\;\;\text{and}\;\;\;\;(U \cap V)^\bot = U^\bot + V^\bot\text{.}$

In fact, the collection of subspaces of Rⁿ satisfy all of the axioms for a Boolean algebra, with intersection as AND, sum as OR, and orthogonal complement as NOT. In Logic, De Morgan's laws or De Morgan's theorem are rules in Formal logic relating pairs of dual Logical operators in a systematic manner expressed In Abstract algebra, a Boolean algebra or Boolean lattice is a complemented distributive lattice.

Notes

^ Linear algebra, as discussed in this article, is a very well-established mathematical discipline for which there are many sources. Linear algebra is the branch of Mathematics concerned with In Mathematics, a vector space (or linear space) is a collection of objects (called vectors) that informally speaking may be scaled and added The concept of a linear subspace (or vector subspace) is important in Linear algebra and related fields of Mathematics. In Geometry, a flat is a subset of ''n''-dimensional space that is congruent to a Euclidean space of lower Dimension. Almost all of the material in this article can be found in Lay 2005, Meyer 2001, and Strang 2005.
^ This equation uses set-builder notation. In Set theory and its applications to Logic, Mathematics, and Computer science, set-builder notation (sometimes simply "set notation" The same notation will be used throughout this article.
^ To add to the confusion, there is also an object called a row vector, usually written [x₁ x₂ ··· x_n]. Some books identify ordered tuples with row vectors instead of column vectors.
^ The requirement that S contains the zero vector is equivalent to requiring that S is nonempty. In Mathematics, and more specifically Set theory, the empty set is the unique set having no ( Zero) members (Once S contains any single vector v it must contain 0v by property 3, and therefore must contain the zero vector. )
^ The second and third requirements can be combined into the following statement: If u and v are elements of S and b and c are scalars, then bu + cv is an element of S.
^ This definition is often stated differently: vectors v₁,. . . ,v_k are linearly independent if t₁v₁ + ··· + t_kv_k ≠ 0 for (t₁, t₂, . . . , t_k) ≠ (0, 0, . . . , 0). The two definitions are equivalent.
^ That is, the intersection of generic subspaces U, V ⊂ Rⁿ has dimension dim(U) + dim(V) − n, or dimension zero if this number is negative.
^ That is, the sum of two generic subspaces U, V − Rⁿ has dimension dim(U) + dim(V), or dimension n if this number exceeds n.

References

See also: List of linear algebra references

Textbooks

Lay, David C. This is a list of selected references for Linear algebra, an important branch of Mathematics. (August 22, 2005), Linear Algebra and Its Applications (3rd ed. ), Addison Wesley, ISBN 978-0321287137
Meyer, Carl D. (February 15, 2001), Matrix Analysis and Applied Linear Algebra, Society for Industrial and Applied Mathematics (SIAM), ISBN 978-0898714548 . Available online at http://www.matrixanalysis.com/DownloadChapters.html
Strang, Gilbert (July 19, 2005), Linear Algebra and Its Applications (4th ed. ), Brooks Cole, ISBN 978-0030105678

Free Online books

Beezer, Rob, A First Course in Linear Algebra
Connell, Edwin H. , Elements of Abstract and Linear Algebra
Hefferon, Jim, Linear Algebra excellent textbook with complete solutions manual

External links

MIT Linear Algebra Lecture on the Four Fundamental Subspaces at Google Video, from MIT OpenCourseWare

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Note on vectors and Rn