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Mathematics · unit version 1

Linear algebra over finite fields

Solve modular systems, count bases and subspaces, and test spectral claims with the scalar field stated explicitly. These finite-field models retain the distinction between linear algebra and arithmetic over composite residue rings.

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Readiness and preparation

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When is multiplication by a nonzero residue reversible?

Elimination must use legal inverses rather than ordinary fraction division.

Compare reasoning and find a refresher

Modulo a prime p every nonzero residue has a multiplicative inverse. Modulo a composite integer a nonzero element may not be invertible; use the gcd criterion.

Review modular inverses and cancellation

How does rank determine the dimension of a solution family?

Finite-field counting turns a basis of free directions into an exact number of choices.

Compare reasoning and find a refresher

For a linear map with n input coordinates and rank r, the kernel has dimension n−r. A nonempty solution set is a translate of that kernel, not usually a subspace itself.

Review rank-nullity and pivot columns

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Systems, subspaces and changing scalars

The mixed assessment has separate response history and review criteria; it does not change lesson participation records.

Lesson sequence

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  1. Eliminate over finite fields and count solution fibers

    Lesson details and objectives

    Use reversible row operations to classify modular systems, then prove how rank counts both attainable outputs and solutions above each output.

    • Perform elimination over a prime field with valid inverses and detect parameter-dependent consistency.
    • Prove that a consistent fiber is a translate of the kernel and count fibers and images over any finite field.
    • Distinguish field elimination from arithmetic over a composite residue ring.

    6 practice tasks.

  2. Count bases, subspaces and invertible matrices

    Lesson details and objectives

    Count independent vectors sequentially, then remove the exact multiplicity of bases to count subspaces over a finite field.

    • Prove the product formulas for independent ordered tuples and invertible matrices over a finite field.
    • Derive the Gaussian binomial count by counting each subspace through its ordered bases, including empty-dimensional boundaries.
    • Compute invertibility probabilities under explicit uniform independent sampling and diagnose invalid subset or dependence arguments.

    6 practice tasks.

  3. Eigenvalues change with the field

    Lesson details and objectives

    Keep the scalar field visible while solving eigenvector equations, constructing extension fields and testing diagonalization.

    • Compute the eigenspaces of one integer matrix after reduction in three different characteristics.
    • Construct eigenvectors in a quadratic extension and distinguish extension of scalars from changing characteristic.
    • Prove the distinct-eigenvalue criterion and diagnose repeated-root cases using eigenspace dimension.

    6 practice tasks.