In linear algebra, what is an eigenvalue?

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Multiple Choice

In linear algebra, what is an eigenvalue?

Explanation:
An eigenvalue is a scalar λ for which there exists a nonzero vector x such that A x = λ x. In other words, along the direction of x, the linear transformation A only stretches by a factor λ (or compresses if |λ| < 1) and does not change the direction of x. The vector x is called an eigenvector corresponding to that eigenvalue. This concept is tied to how a matrix acts: some directions are preserved under the transformation, just scaled. For example, a diagonal matrix with entries 2 and 3 has eigenvalues 2 and 3 with eigenvectors along the coordinate axes, because applying the matrix to those basis vectors simply scales them. Notes on the other ideas: the determinant is the product of all eigenvalues (counting multiplicities), not a single eigenvalue; the trace is the sum of all eigenvalues, not an eigenvalue itself; and if you invert A (when possible), the eigenvalues become the reciprocals 1/λ of the original ones (for nonzero λ).

An eigenvalue is a scalar λ for which there exists a nonzero vector x such that A x = λ x. In other words, along the direction of x, the linear transformation A only stretches by a factor λ (or compresses if |λ| < 1) and does not change the direction of x. The vector x is called an eigenvector corresponding to that eigenvalue.

This concept is tied to how a matrix acts: some directions are preserved under the transformation, just scaled. For example, a diagonal matrix with entries 2 and 3 has eigenvalues 2 and 3 with eigenvectors along the coordinate axes, because applying the matrix to those basis vectors simply scales them.

Notes on the other ideas: the determinant is the product of all eigenvalues (counting multiplicities), not a single eigenvalue; the trace is the sum of all eigenvalues, not an eigenvalue itself; and if you invert A (when possible), the eigenvalues become the reciprocals 1/λ of the original ones (for nonzero λ).

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