Stanford Convex Optimization
Master convex optimization theory and applications in engineering, machine learning, and scientific computing.
Master convex optimization theory and applications in engineering, machine learning, and scientific computing.
This advanced course provides comprehensive coverage of convex optimization theory and applications. Students learn to recognize and solve convex optimization problems across various fields including signal processing, machine learning, and engineering design. The curriculum covers convex sets, functions, duality theory, interior-point methods, and practical applications. Through a combination of theoretical foundations and hands-on implementation using MATLAB and CVX, students develop expertise in applying optimization techniques to real-world problems.
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What you'll learn
Recognize and formulate convex optimization problems
Master fundamental theory of convex analysis and optimization
Implement solutions using modern computational tools
Apply optimization techniques to real-world engineering problems
Understand duality theory and interior-point methods
Develop practical skills for research applications
Skills you'll gain
This course includes:
PreRecorded video
Programming assignments, problem sets
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Limited Access access
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Module Description
This comprehensive course focuses on recognizing and solving convex optimization problems in various applications. The curriculum covers fundamental concepts including convex sets, functions, and optimization problems, along with advanced topics such as duality theory and interior-point methods. Students learn practical applications in signal processing, machine learning, control engineering, circuit design, and finance. The course emphasizes both theoretical understanding and computational implementation using MATLAB and CVX.
Instructors
1 Course
A Pioneering Authority in Optimization Theory and Engineering Education
Stephen P. Boyd serves as the Samsung Professor of Engineering at Stanford University, where he has revolutionized the field of convex optimization since joining the faculty in 1985. After earning his AB in Mathematics summa cum laude from Harvard in 1980 and Ph.D. in Electrical Engineering from UC Berkeley in 1985, he has built an extraordinary career combining research excellence with exceptional teaching. His contributions include four influential books and widely-used open-source tools like CVX and CVXPY, with his "Convex Optimization" text garnering over 21,000 citations. His graduate optimization course attracts 300 students from 25 departments, reflecting his ability to make complex concepts accessible across disciplines. His work extends to practical applications, with his group's CVXGEN software being used in SpaceX's Falcon rockets for precision landing. His excellence in teaching has earned him numerous honors, including the Walter J. Gores Award, Stanford's highest teaching recognition, and the IEEE James H. Mulligan Jr. Education Medal. As a member of the National Academy of Engineering and holder of honorary doctorates from multiple institutions, he continues to advance the field through his research in optimization applications for control, signal processing, machine learning, and finance while maintaining his commitment to innovative education.
1 Course
A Multidisciplinary Expert in Imaging Systems and Computer Vision
Henryk Blasinski served as a Teaching Assistant in Stanford University's Electrical Engineering Department while pursuing his Ph.D., bringing expertise in image processing, human visual systems, and machine learning. After earning his M.Sc. degrees from both the Institut Superieur d'Electronique de Paris and Technical University of Lodz, he contributed to Stanford's academic community through teaching roles in courses including Applied Vision and Image Systems, Signals and Systems, and Linear Dynamical Systems. His research focused on optimizing image acquisition systems, particularly for autonomous driving applications, working closely with the Stanford Center for Image Systems Engineering. Beyond his academic work, he demonstrated multilingual proficiency in Polish, English, French, and Japanese, while pursuing interests in sailing, skiing, and mountaineering. His contributions to the field include significant publications on color barcodes, barrel distortion removal, and low-power FPGA design, combining theoretical expertise with practical applications in imaging technology.
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