Determine Whether V1 V2 V3 Is A Basis For R3

Determining Whether V1, V2, V3 is a Basis for R³

Determining if a set of vectors {v₁, v₂, v₃} forms a basis for R³ is a fundamental concept in linear algebra. This article will provide a comprehensive guide on how to perform this determination, covering the underlying theory and providing step-by-step examples. Understanding this process is crucial for mastering linear transformations, solving systems of linear equations, and comprehending the structure of vector spaces. We'll explore multiple methods, including checking for linear independence and spanning the entire R³ space.

Introduction to Bases in R³

Before diving into the methods, let's define some key terms. R³ represents the three-dimensional real vector space, meaning all vectors with three real number components. A basis for a vector space is a set of linearly independent vectors that span the entire space. This means any vector in R³ can be expressed as a unique linear combination of the basis vectors. Linear independence ensures that no vector in the basis can be written as a linear combination of the others.

Method 1: Checking for Linear Independence and Spanning R³

This method involves two key steps:

1. Checking for Linear Independence: Vectors v₁, v₂, and v₃ are linearly independent if the only solution to the equation a₁v₁ + a₂v₂ + a₃v₃ = 0 (the zero vector) is a₁ = a₂ = a₃ = 0. This can be determined by creating an augmented matrix and performing Gaussian elimination (row reduction). If the resulting matrix has a pivot in every column (excluding the augmented column), the vectors are linearly independent.

2. Checking for Spanning R³: If the vectors are linearly independent in R³, and there are three vectors, then they automatically span R³. This is a consequence of the dimension of R³ being 3. Any three linearly independent vectors in R³ will form a basis.

Step-by-Step Example:

Let's consider the vectors:

• v₁ = (1, 0, 0)
• v₂ = (0, 1, 0)
• v₃ = (0, 0, 1)

Step 1: Checking for Linear Independence:

We set up the equation a₁v₁ + a₂v₂ + a₃v₃ = 0:

a₁(1, 0, 0) + a₂(0, 1, 0) + a₃(0, 0, 1) = (0, 0, 0)

This simplifies to:

(a₁, a₂, a₃) = (0, 0, 0)

The only solution is a₁ = a₂ = a₃ = 0. Therefore, the vectors are linearly independent.

Step 2: Checking for Spanning R³:

Since we have three linearly independent vectors in R³, they automatically span R³. This is because the dimension of R³ is 3, and any set of three linearly independent vectors in a 3-dimensional space will form a basis.

Conclusion: Since v₁, v₂, and v₃ are linearly independent and span R³, they form a basis for R³.

Method 2: Using the Determinant of a Matrix

This method is particularly efficient for checking if three vectors form a basis in R³. We construct a matrix where each vector forms a column:

A = | 1  2  3 |
    | 4  5  6 |
    | 7  8  9 |

If the determinant of this matrix is non-zero, then the vectors are linearly independent and thus form a basis for R³. If the determinant is zero, the vectors are linearly dependent, and they do not form a basis.

Step-by-Step Example:

Let's use the same vectors as before:

• v₁ = (1, 0, 0)
• v₂ = (0, 1, 0)
• v₃ = (0, 0, 1)

The matrix A becomes:

A = | 1  0  0 |
    | 0  1  0 |
    | 0  0  1 |

The determinant of this matrix is 1 (it's an identity matrix). Since the determinant is non-zero, the vectors are linearly independent and form a basis for R³.

Another Example with a different set of vectors:

Let's consider:

• v₁ = (1, 2, 3)
• v₂ = (4, 5, 6)
• v₃ = (7, 8, 9)

The matrix A is:

A = | 1  4  7 |
    | 2  5  8 |
    | 3  6  9 |

Calculating the determinant of A (using cofactor expansion or other methods) yields 0. Since the determinant is 0, the vectors are linearly dependent and do not form a basis for R³.

Method 3: Gaussian Elimination and Row Reduction

This method directly addresses linear independence. We create an augmented matrix with the vectors as columns and the zero vector as the augmented column. Then, we perform Gaussian elimination (row reduction) to obtain the row echelon form.

If we have a pivot in every column (excluding the augmented column), then the vectors are linearly independent, and since we are in R³, they will span the space and form a basis. If there is a column without a pivot (excluding the augmented column), the vectors are linearly dependent, and thus, do not form a basis.

Step-by-Step Example:

Using the vectors v₁ = (1, 2, 3), v₂ = (4, 5, 6), v₃ = (7, 8, 9):

The augmented matrix is:

| 1  4  7 | 0 |
| 2  5  8 | 0 |
| 3  6  9 | 0 |

Performing row reduction:

• R2 = R2 - 2R1
• R3 = R3 - 3R1

| 1  4  7 | 0 |
| 0 -3 -6 | 0 |
| 0 -6 -12| 0 |

• R3 = R3 - 2R2

| 1  4  7 | 0 |
| 0 -3 -6 | 0 |
| 0  0  0 | 0 |

Notice that the third column (corresponding to v₃) does not have a pivot. This means the vectors are linearly dependent and do not form a basis for R³.

Frequently Asked Questions (FAQ)

Q: What if I have more than three vectors? If you have more than three vectors in R³, they cannot be linearly independent, and thus cannot form a basis. The maximum number of linearly independent vectors in R³ is three.

Q: What if I have fewer than three vectors? If you have fewer than three vectors, they cannot span R³, and thus cannot form a basis.

Q: How does this relate to solving systems of linear equations? The methods used to determine linear independence (Gaussian elimination, determinant calculation) are directly related to solving systems of linear equations. The existence and uniqueness of solutions are tied to the linear independence of the vectors involved.

Q: Why is linear independence crucial for a basis? Linear independence ensures that each vector in the basis contributes uniquely to the representation of any vector in the space. Without linear independence, you could express the same vector in multiple ways using the basis vectors, losing the unique representation property that defines a basis.

Q: What are some applications of determining bases? Finding bases is fundamental to many applications in linear algebra, including: understanding linear transformations, solving systems of linear equations, dimensionality reduction (principal component analysis), cryptography, and computer graphics.

Conclusion

Determining whether a set of vectors forms a basis for R³ is a cornerstone of linear algebra. We have explored three primary methods: checking for linear independence and spanning, using determinants, and using Gaussian elimination. Each method offers a unique approach to solve this problem, and the choice of method often depends on the specific context and the properties of the given vectors. Mastering these methods is crucial for a strong understanding of linear algebra and its diverse applications. Remember that for a set of vectors to form a basis in R³, they must be both linearly independent and span the entire R³ space. The key is to ensure that no vector in the set is a linear combination of others and that any vector in R³ can be expressed uniquely as a linear combination of the vectors in the set.