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TIME: TR 8-9:15am (Wilson 1-117)
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TEXTBOOK:
Elementary Linear Algebra, H. Anton, C. Rorres (9th edition)
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INSTRUCTOR: Mark Pernarowski
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OFFICE: Wilson 2-236
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OFFICE HOURS: Schedule
M,F 9-10; Tu 10-11, W 12-1
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PHONE: 994-5356
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GRADING SYSTEM:
The course % will determined as follows:
| | | Total Points |
| Midterm | M | 100 |
| Final | F | 100 |
| Homework | HW | 200 |
| | | |
| Course % | = | (M+F+HW)/4 |
Homework due dates and the date for the midterm
will be announced in class.
Exact content of the exams will also be announced
in class. The final exam is NOT comprehensive.
There is no set schedule for when homework will
be assigned. Some assignments will be longer
than others. As a result, the HW grade above will
be the percentage obtained.
Topics Covered:
- Vectors in R^n
- Axiomatic Definition
- Dot Product, Norm, Distance
- Linear Transformations from R^n to R^m
- Reflection, Rotation, Projection
- Generating matrices from standard basis
- Inverse, 1-1, equivalent conditions
- General Vector Spaces
- Axiomatic Definitions
- Examples of different addition and scalar multiplication
- R^n, P_n, M_{nn}, and C^n(R) function spaces
- Subspace Theorem
- Spanning sets, Linear Independence, Basis
- Plus/Minus Theorems
- Basis, dimension Theorems
- Coordinates relative to a basis
- Finding bases in coordinate spaces
- Fundamental Matrix Spaces
- row(A), col(A), N(A), N(A^T)
- Relation to coordinate spaces of subspaces
- Inner Product Spaces
- Real inner product spaces, norm, distance
- Cauchy-Schwartz inequality
- Orthogonality, orthogonal bases
- Gram-Schmidt process
- Projections onto subspaces
- Orthogonal Complement Spaces
- Sum and Direct sum
- Orthogonal Decomposition of Finite Dimensional Spaces
- Orthogonality of Fundamental Matrix Spaces
- Relation to Coordinate Spaces
- Eigenvalues and Eigenvectors
- Definition, Characteristic Polynomials
- Algebraic and Geometric Multiplicity of Eigenspaces
- Diagonalizability Theorems
- Symmetric Matrices, Orthogonal Diagonalization
- Functions of Symmetric Matrices, Iterations
- Linear Transformations
- Definitions and Examples
- Images from basis vectors, Compositions
- Kernel, Range
- Generating matrices and commuting diagrams in coordinate space
- Inverse transformations, linearity
- Eigenfunctions, values for general transformations
- Differential operator examples
Homework Assignments
