Math 563, Measure Theory, Fall 2016

General Information

Instructor: Matthew Blair
Email Address: blair ["at"]
Course Web Page:
Office: SMLC 330
Office Hours: Monday 3-5pm and Tuesday 2:15-3:15pm.

Text: Real Analysis: Measure Theory, Integration, and Hilbert Spaces by Elias M. Stein and Rami Shakarchi, Princeton University Press, 2005. It is expected that we will cover the main topics in Chapters 1, 2, 3, and 6. L^p spaces and other topics will be covered if time permits.

The website for the text, along with a PDF of Chapter 1 can be found here.

Meeting times/location: MWF 1-1:50pm, SMLC 356.

Prerequisites: Real analysis at the level of Math 510. Consult the instructor with any questions.

Course Description

Towards the end of the nineteenth century, it was realized that the Riemann integral was not robust enough to handle all the needed applications. For example, Riemann's theory does not deal with convergence issues particularly well, such as "passing the limit under the integral sign". However, this is important for many applications of integration, including developing a rigorous foundation for Fourier analysis. A successful alternative was discovered by Henri Lebesgue in his 1902 thesis, who proposed to define the integral by more or less partitioning the range instead of the domain. In subsequent years this notion of integration has seen significant refinements and generalization.

In this course, we will begin by defining Lebesgue measure on Euclidean space and use this to integrate functions. We will then explore several important consequences of the Lebesgue integral such as convergence, differentiation, and averaging theorems. Finally, we will study abstract measure theory, where one can begin to consider notions of measure and integration over sets which may not be Euclidean in nature. If time permits, we will also discuss Lp spaces. This course will provide a solid (and often essential) foundation for students interested in differential equations, probability, functional analysis, and harmonic analysis.


Assignment #1--Due Wednesday, September 7

See handout

Assignment #2--Due Wednesday, September 14

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Assignment #3--Due Wednesday, September 28

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Assignment #4--Due Wednesday, October 12

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Assignment #5--Due Wednesday, October 26

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Assignment #6--Due Wednesday, November 9

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Assignment #7--Due Wednesday, November 23

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Assignment #8--Due Friday, December 9

See handout