Effective: 2017-08-01

Course Description

The course outline below was developed as part of a statewide standardization process.

General Course Purpose

The general purpose is to give the student a solid grasp of the methods and applications of linear algebra, and to prepare the student for further coursework in mathematics, engineering, computer science and the sciences.

Course Prerequisites/Corequisites

Prerequisite: Completion of MTH 263 or equivalent with a grade of B or better or MTH 264 or equivalent with a grade of C or better.

Course Objectives

• Matrices and Systems of Equations
• Use correct matrix terminology to describes various types and features of matrices (triangular, symmetric, row echelon form, et.al.)
• Use Gauss-Jordan elimination to transform a matrix into reduced row echelon form
• Determine conditions such that a given system of equations will have no solution, exactly one solution, or infinitely many solutions
• Write the solution set for a system of linear equations by interpreting the reduced row echelon form of the augmented matrix, including expressing infinitely many solutions in terms of free parameters
• Write and solve a system of equations modeling real world situations such as electric circuits or traffic flow
• Matrix Operations and Matrix Inverses
• Perform the operations of matrix-matrix addition, scalar-matrix multiplication, and matrix-matrix multiplication on real and complex valued matrices
• State and prove the algebraic properties of matrix operations
• Find the transpose of a real valued matrix and the conjugate transpose of a complex valued matrix
• Identify if a matrix is symmetric (real valued)
• Find the inverse of a matrix, if it exists, and know conditions for invertibility.
• Use inverses to solve a linear system of equations
• Determinants
• Compute the determinant of a square matrix using cofactor expansion
• State, prove, and apply determinant properties, including determinant of a product, inverse, transpose, and diagonal matrix
• Use the determinant to determine whether a matrix is singular or nonsingular
• Use the determinant of a coefficient matrix to determine whether a system of equations has a unique solution
• Norm, Inner Product, and Vector Spaces
• Perform operations (addition, scalar multiplication, dot product) on vectors in Rn and interpret in terms of the underlying geometry
• Determine whether a given set with defined operations is a vector space
• Basis, Dimension, and Subspaces
• Determine whether a vector is a linear combination of a given set; express a vector as a linear combination of a given set of vectors
• Determine whether a set of vectors is linearly dependent or independent
• Determine bases for and dimension of vector spaces/subspaces and give the dimension of the space
• Prove or disprove that a given subset is a subspace of Rn
• Reduce a spanning set of vectors to a basis
• Extend a linearly independent set of vectors to a basis
• Find a basis for the column space or row space and the rank of a matrix
• Make determinations concerning independence, spanning, basis, dimension, orthogonality and orthonormality with regards to vector spaces
• Linear Transformations
• Use matrix transformations to perform rotations, reflections, and dilations in Rn
• Verify whether a transformation is linear
• Perform operations on linear transformations including sum, difference and composition
• Identify whether a linear transformation is one-to-one and/or onto and whether it has an inverse
• Find the matrix corresponding to a given linear transformation T: Rn -> Rm
• Find the kernel and range of a linear transformation
• State and apply the rank-nullity theorem
• Compute the change of basis matrix needed to express a given vector as the coordinate vector with respect to a given basis
• Eigenvalues and Eigenvectors
• Calculate the eigenvalues of a square matrix, including complex eigenvalues.
• Calculate the eigenvectors that correspond to a given eigenvalue, including complex eigenvalues and eigenvectors.
• Compute singular values
• Determine if a matrix is diagonalizable
• Diagonalize a matrix

Major Topics to be Included

• Matrices and Systems of Equations
• Matrix Operations and Matrix Inverses
• Determinants
• Norm, Inner Product, and Vector Spaces
• Basis, Dimension, and Subspaces
• Linear Transformations
• Eigenvalues and Eigenvectors