Numerical Analysis (CS 450) Spring 2024

Quizzes

Posted here by start of each lecture. Due 7 days from start of lecture, Generally, we will go over all or most quiz questions during the lecture.

Quiz 1: Error, Conditioning, and Course Policies »

Quiz 2: Floating Point Arithmetic »

Quiz 3: Matrix Norms and Conditioning, Triangular Systems »

Quiz 4: Solving Linear Systems »

Quiz 5: Solving Linear Least Squares Problems »

Quiz 6: Linear Least Squares Problems and Low-Rank Approximation »

Quiz 7: Eigenvalues, Conditioning, and Power Iteration »

Quiz 8: Eigenvalue Decomposition Algorithms »

Quiz 9: Nonlinear Equations »

Quiz 10: Nonlinear Optimization »

Quiz 11: Constrained Nonlinear Optimization »

Quiz 12: Interpolation »

Quiz 13: Orthogonal Polynomials, Piecewise Interpolation, and Quadrature »

Quiz 14: Composite Quadrature and Numerical Differentiation »

Quiz 15: Characterization of ODEs and Euler Methods »

Quiz 16: Initial Value Problems for Ordinary Differential Equations »

Quiz 17: Boundary Value Problems for Ordinary Differential Equations »

Quiz 18: BVPs and Classification of PDEs »

Homework

Homework 1 »

Homework 2 »

Homework 3 »

Homework 4 »

Homework 5 (worth 2/3 value of regular homework, last question extra credit) »

4-Credit Hour Homeworks

For the 4-credit hour section, addendum problems are worth 1/4 of the total credit for each assignment. For the 3-credit hour section, these problems may be completed for extra credit, with half of the value (1/6 of the assignment value).

Homework 1 Addendum »

Homework 2 Addendum »

Homework 3 Addendum »

Homework 4 Addendum »

Course Outline

Team

Statement on CS CARES, Values, and Code of Conduct

All members of the Illinois Computer Science department---faculty, staff, and students---are expected to adhere to the CS Values and Code of Conduct. The CS CARES Committee is available to serve as a resource to help people who are concerned about or experience a potential violation of the Code. If you experience such issues, please contact the CS CARES Committee. The instructor of this course are also available for issues related to this class.

Textbook

Computing

We will be using Python with the libraries numpy, scipy and matplotlib for in-class work and assignments. No other languages are permitted. Python has a very gentle learning curve, so you should feel at home even if you've never done any work in Python.

Running Code on your Own Computer

While running code in this online system should technically suffice to do your work for this class, you may find it useful to also install Python on your own computer.

The recommended and perhaps one of the easier ways of doing so involves downloading the Anaconda Python distribution. Note that this is a commercial product (even if it is free of charge), and this is not intended as an endorsement of the company or the product. Note that we cannot promise to provide technical support for this installation.

Download Anaconda Python »

Another way to obtain a Python installation is through a virtual machine image:

Download Virtual Machine »

Grading Policies

View policies »

Python Help

(see section 1 of the outline for more)

• Python tutorial
• Facts and myths about Python names and values
• Learn Python the hard way
• Project Euler (Lots of practice problems)
• From Python to Numpy
• PythonTutor (Execute Python step-by-step, with pictures)

Python workshop material

• Scipy lecture notes
• CSE workshop training material
• Tutorial material

Numpy Help

(see section 1 of the outline for more)

• Introduction to Python for Science
• The SciPy lectures
• The Numpy MedKit by Stéfan van der Walt
• The Numpy User Guide by Travis Oliphant
• Numpy/Scipy documentation
• More in this reddit thread
• An introduction to Numpy and SciPy