This article needs additional citations for verification. (January 2019) (Learn how and when to remove this template message) 
In the theory of vector spaces, a set of vectors is said to be linearly dependent if there is a nontrivial linear combination of the vectors that equals the zero vector. If no such linear combination exists, then the vectors are said to be linearly independent. These concepts are central to the definition of dimension.^{[1]}
A vector space can be of finite dimension or infinite dimension depending on the maximum number of linearly independent vectors. The definition of linear dependence and the ability to determine whether a subset of vectors in a vector space is linearly dependent are central to determining the dimension of a vector space.
Definition
A sequence of vectors from a vector space V is said to be linearly dependent, if there exist scalars not all zero, such that
where denotes the zero vector.
Notice that if not all of the scalars are zero, then at least one is nonzero, say , in which case this equation can be written in the form
Thus, is shown to be a linear combination of the remaining vectors.
A sequence of vectors is said to be linearly independent if the equation
can only be satisfied by for This implies that no vector in the sequence can be represented as a linear combination of the remaining vectors in the sequence. In other words, a sequence of vectors is linearly independent if the only representation of as a linear combination of its vectors is the trivial representation in which all the scalars are zero.^{[2]} Even more concisely, a sequence of vectors is linearly independent if and only if can be represented as a linear combination of its vectors in a unique way.
The alternative definition, that a sequence of vectors is linearly dependent if and only if some vector in that sequence can be written as a linear combination of the other vectors, is only useful when the sequence contains two or more vectors. When the sequence contains no vectors or only one vector, the original definition is used.
Infinite dimensions
In order to allow the number of linearly independent vectors in a vector space to be countably infinite, it is useful to define linear dependence as follows. More generally, let V be a vector space over a field K, and let {v_{i}  i ∈ I} be a family of elements of V indexed by the set I. The family is linearly dependent over K if there exists a nonempty finite subset J ⊆ I and a family {a_{j}  j ∈ J} of elements of K, all nonzero, such that
A set X of elements of V is linearly independent if the corresponding family {x}_{x∈X} is linearly independent. Equivalently, a family is dependent if a member is in the closure of the linear span of the rest of the family, i.e., a member is a linear combination of the rest of the family. The trivial case of the empty family must be regarded as linearly independent for theorems to apply.
A set of vectors which is linearly independent and spans some vector space, forms a basis for that vector space. For example, the vector space of all polynomials in x over the reals has the (infinite) subset {1, x, x^{2}, ...} as a basis.
Geometric meaning
A geographic example may help to clarify the concept of linear independence. A person describing the location of a certain place might say, "It is 3 miles north and 4 miles east of here." This is sufficient information to describe the location, because the geographic coordinate system may be considered as a 2dimensional vector space (ignoring altitude and the curvature of the Earth's surface). The person might add, "The place is 5 miles northeast of here." Although this last statement is true, it is not necessary.
In this example the "3 miles north" vector and the "4 miles east" vector are linearly independent. That is to say, the north vector cannot be described in terms of the east vector, and vice versa. The third "5 miles northeast" vector is a linear combination of the other two vectors, and it makes the set of vectors linearly dependent, that is, one of the three vectors is unnecessary.
Also note that if altitude is not ignored, it becomes necessary to add a third vector to the linearly independent set. In general, n linearly independent vectors are required to describe all locations in ndimensional space.
Evaluating linear independence
The zero vector
If one or more vectors from a given sequence of vectors is the zero vector then the vector are necessarily linearly dependent (and consequently, they are not linearly independent). To see why, suppose that is an index (i.e. an element of ) such that Then let (alternatively, letting be equal any other nonzero scalar will also work) and then let all other scalars be (explicitly, this means that for any index other than (i.e. for ), let so that consequently ). Simplifying gives:
Because not all scalars are zero (in particular, ), this proves that the vectors are linearly dependent.
As a consequence, the zero vector can not possibly belong to any collection of vectors that is linearly independent.
Now consider the special case where the sequence of has length (i.e. the case where ). A collection of vectors that consists of exactly one vector is linearly dependent if and only if that vector is zero. Explicitly, if is any vector then the sequence (which is a sequence of length ) is linearly dependent if and only if ; alternatively, the collection is linearly independent if and only if
Linear dependence and independence of two vectors
This example considers the special case where there are exactly two vector and from some real or complex vector space. The vectors and are linearly dependent if and only if at least one of the following is true:
 is a scalar multiple of (explicitly, this means that there exists a scalar such that ) or
 is a scalar multiple of (explicitly, this means that there exists a scalar such that ).
If then by setting we have (this equality holds no matter what the value of is), which shows that (1) is true in this particular case. Similarly, if then (2) is true because If (for instance, if they are both equal to the zero vector ) then both (1) and (2) are true (by using for both).
If then is only possible if and ; in this case, it is possible to multiply both sides by to conclude This shows that if and then (1) is true if and only if (2) is true; that is, in this particular case either both (1) and (2) are true (and the vectors are linearly dependent) or else both (1) and (2) are false (and the vectors are linearly independent). If but instead then at least one of and must be zero. Moreover, if exactly one of and is (while the other is nonzero) then exactly one of (1) and (2) is true (with the other being false).
The vectors and are linearly independent if and only if is not a scalar multiple of and is not a scalar multiple of .
Vectors in R^{2}
Three vectors: Consider the set of vectors and then the condition for linear dependence seeks a set of nonzero scalars, such that
or
Row reduce this matrix equation by subtracting the first row from the second to obtain,
Continue the row reduction by (i) dividing the second row by 5, and then (ii) multiplying by 3 and adding to the first row, that is
Rearranging this equation allows us to obtain
which shows that nonzero a_{i} exist such that can be defined in terms of and Thus, the three vectors are linearly dependent.
Two vectors: Now consider the linear dependence of the two vectors and and check,
or
The same row reduction presented above yields,
This shows that which means that the vectors v_{1} = (1, 1) and v_{2} = (−3, 2) are linearly independent.
Vectors in R^{4}
In order to determine if the three vectors in
are linearly dependent, form the matrix equation,
Row reduce this equation to obtain,
Rearrange to solve for v_{3} and obtain,
This equation is easily solved to define nonzero a_{i},
where can be chosen arbitrarily. Thus, the vectors and are linearly dependent.
Alternative method using determinants
An alternative method relies on the fact that vectors in are linearly independent if and only if the determinant of the matrix formed by taking the vectors as its columns is nonzero.
In this case, the matrix formed by the vectors is
We may write a linear combination of the columns as
We are interested in whether AΛ = 0 for some nonzero vector Λ. This depends on the determinant of , which is
Since the determinant is nonzero, the vectors and are linearly independent.
Otherwise, suppose we have vectors of coordinates, with Then A is an n×m matrix and Λ is a column vector with entries, and we are again interested in AΛ = 0. As we saw previously, this is equivalent to a list of equations. Consider the first rows of , the first equations; any solution of the full list of equations must also be true of the reduced list. In fact, if ⟨i_{1},...,i_{m}⟩ is any list of rows, then the equation must be true for those rows.
Furthermore, the reverse is true. That is, we can test whether the vectors are linearly dependent by testing whether
for all possible lists of rows. (In case , this requires only one determinant, as above. If , then it is a theorem that the vectors must be linearly dependent.) This fact is valuable for theory; in practical calculations more efficient methods are available.
More vectors than dimensions
If there are more vectors than dimensions, the vectors are linearly dependent. This is illustrated in the example above of three vectors in
Natural basis vectors
Let and consider the following elements in , known as the natural basis vectors:
Then are linearly independent.
Proof


Suppose that are real numbers such that Since then for all 
Linear independence of functions
Let be the vector space of all differentiable functions of a real variable . Then the functions and in are linearly independent.
Proof
Suppose and are two real numbers such that
Take the first derivative of the above equation:
for all values of We need to show that and In order to do this, we subtract the first equation from the second, giving . Since is not zero for some , It follows that too. Therefore, according to the definition of linear independence, and are linearly independent.
Space of linear dependencies
A linear dependency or linear relation among vectors v_{1}, ..., v_{n} is a tuple (a_{1}, ..., a_{n}) with n scalar components such that
If such a linear dependence exists with at least a nonzero component, then the n vectors are linearly dependent. Linear dependencies among v_{1}, ..., v_{n} form a vector space.
If the vectors are expressed by their coordinates, then the linear dependencies are the solutions of a homogeneous system of linear equations, with the coordinates of the vectors as coefficients. A basis of the vector space of linear dependencies can therefore be computed by Gaussian elimination.
Affine independence
A set of vectors is said to be affinely dependent if at least one of the vectors in the set can be defined as an affine combination of the others. Otherwise, the set is called affinely independent. Any affine combination is a linear combination; therefore every affinely dependent set is linearly dependent. Conversely, every linearly independent set is affinely independent.
Consider a set of vectors of size each, and consider the set of augmented vectors of size each. The original vectors are affinely independent if and only if the augmented vectors are linearly independent.^{[3]}^{:256}
See also: affine space.
See also
 Matroid – Abstract structure that models and generalizes linear independency
References
 ^ G. E. Shilov, Linear Algebra (Trans. R. A. Silverman), Dover Publications, New York, 1977.
 ^ Friedberg, Insel, Spence, Stephen, Arnold, Lawrence (2003). Linear Algebra. Pearson, 4th Edition. pp. 48–49. ISBN 0130084514.CS1 maint: multiple names: authors list (link)
 ^ Lovász, László; Plummer, M. D. (1986), Matching Theory, Annals of Discrete Mathematics, 29, NorthHolland, ISBN 0444879161, MR 0859549
External links
 "Linear independence", Encyclopedia of Mathematics, EMS Press, 2001 [1994]
 Linearly Dependent Functions at WolframMathWorld.
 Tutorial and interactive program on Linear Independence.
 Introduction to Linear Independence at KhanAcademy.