MATH1030: Linear algebra I
Rigorous linear algebra notes on systems, matrices, structure, and proof, with interaction used only where it clarifies the mathematics.
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Course contents
MATH1030: Linear algebra I
Rigorous linear algebra notes on systems, matrices, structure, and proof, with interaction used only where it clarifies the mathematics.
Chapter 1Systems of equations1 sections
Chapter 2Matrices and elimination4 sections
Chapter 3Matrix algebra2 sections
Chapter 4Solution structure1 sections
Chapter 5Invertibility1 sections
Chapter 8Eigenvalues and diagonalization3 sections
Chapter 9Inner products and orthogonality4 sections
Chapter 1
Systems of equations
Learn to read equations as full solution sets.
1.1 Equations and solution sets
Read a linear system as a collection of conditions and describe its full solution set carefully.
Chapter 2
Matrices and elimination
Build matrix intuition and use row reduction with purpose.
2.1 Matrix basics
Build matrix intuition before you row-reduce: size, entries, rows, columns, and arithmetic meaning.
2.2 Augmented matrices and row operations
Translate a system into an augmented matrix and understand what each row operation preserves.
2.3 Gaussian elimination and RREF
See Gaussian elimination as a sequence of purposeful moves, not just memorized mechanics.
2.4 Solution-set types
Classify whether a system has one solution, infinitely many solutions, or no solution by reading its reduced form.
Chapter 3
Matrix algebra
Matrix multiplication, transpose, and structural matrix notation.
3.1 Matrix multiplication and identity matrices
Learn when matrix products are defined, how the row-by-column rule works, and why the identity matrix matters for solving linear systems.
3.2 Transpose and special matrices
Use transpose, symmetry, commuting products, and block notation to read matrix structure rather than treating formulas as isolated tricks.
Chapter 4
Solution structure
Homogeneous systems, null spaces, and the shape of full solution sets.
4.1 Homogeneous systems and null space
Study homogeneous systems carefully, then use null spaces to describe every solution as a structured set rather than a loose list of examples.
Chapter 5
Invertibility
Understand when a matrix can be undone and why that matters.
5.1 Invertible matrices
Connect inverse matrices, row reduction, and the practical meaning of nonsingularity.
Chapter 6
Vector spaces
Move from matrix procedures to the structure of spaces, span, independence, and basis.
6.1 Vector spaces
Start from familiar examples and learn what the vector-space axioms are trying to protect.
6.2 Subspaces
Use the subspace test to separate genuine linear structure from lookalikes that fail closure or miss the zero vector.
6.3 Linear combinations and span
Treat linear combinations as controlled building instructions, then see span as every vector you can build that way.
6.4 Linear dependence and independence
Read dependence as redundancy, and independence as the point where every coefficient truly matters.
6.5 Basis and dimension
See why a basis is the smallest complete coordinate system for a space, and why dimension counts how many directions are really needed.
6.6 Column space, row space, and rank
Use row reduction and basis ideas together to read column space, row space, and rank without confusing what row operations actually preserve.
Chapter 7
Determinants
Determinants, cofactor formulas, and the structural algebra that connects row operations, transpose, and invertibility.
7.1 Determinants and cofactor expansion
Define determinants carefully through minors and cofactors, then learn how cofactor expansion turns one scalar into a precise summary of square-matrix structure.
7.2 Row operations, products, and invertibility
Track exactly how row operations change determinants, then connect that behavior to multiplicativity, inverse matrices, and invertibility tests.
7.3 Transpose, column operations, and Cramer's rule
Use transpose and column operations to read determinants from a second angle, then finish the chapter with adjoints, inverse formulas, and Cramer's rule.
Chapter 8
Eigenvalues and diagonalization
Eigenvalues, eigenspaces, similarity, and diagonalization as the next structural layer after determinants.
8.1 Eigenvalues, eigenvectors, and eigenspaces
Define eigenvalues through the equation Av=λv, then recast the same idea as a null-space and determinant question so the structure becomes computable.
8.2 Diagonalization and similarity
Treat diagonalization as a basis change built from eigenvectors, then use similarity to explain when a matrix can be simplified without changing its essential eigenvalue data.
8.3 Characteristic polynomials and diagonalization tests
Use characteristic polynomials, algebraic and geometric multiplicity, and the distinct-eigenvalue test to decide when eigenvalue data is enough for diagonalization.
Chapter 9
Inner products and orthogonality
Inner products, orthogonality, orthonormal bases, and Gram-Schmidt as the geometric layer after eigenvalues.
9.1 Inner products, norms, and angles
Define the standard inner product and norm on R^m, then connect those formulas to length, angle, and the first structural inequalities.
9.2 Orthogonal sets and orthonormal bases
Use orthogonality to build orthogonal and orthonormal bases, then read coefficients without solving a linear system every time.
9.3 Gram-Schmidt orthogonalization
Apply Gram-Schmidt to turn a basis into an orthogonal or orthonormal basis while preserving the same span.
9.4 Cauchy-Schwarz and triangle inequalities
Study Cauchy-Schwarz and triangle inequalities as the two core estimates that control length, angle, and equality cases in inner-product spaces.