Great thanks for sharing these valuable lectures. Using them and reading Prof. Boyds' book, I not only could ponder the convex optimization concepts and dig through the problems that I am facing in my field (the electricity markets) but also learn how to make the course more attractive for the students even if its a completely serious mathematic course. Thank you, Professor Boyd.
max cut is an optimization problem which could be modeled as SDP and get a approximation ratio of 0.878, i.e 13% off, see www2.cs.duke.edu/courses/fall15/compsci532/scribe_notes/lec17.pdf
wrong,idts. no such thing as interesx or not about it, or interesx thus hear more etc. ts just toolx not interex. ceptu thesex, any be any interesx no matter what, say/can say any no matter what, no such thing as tryx or etc
1:08 Generalized inequality constraints
7:04 Semidefinite program (SDP)
10:55 LP and SOCP as SDP
17:57 Eigenvalue minimization
20:47 Matrix norm minimization
27:28 Vector optimization
33:14 Optimal and Pareto optimal points
38:58 Multi criterion optimization
41:54 Regularized least-squares
44:50 Risk return trade-off in portfolio optimization
54:47 Scalarization
57:51 Scalarization for multiplication problems
1:02:10 Duality
Great thanks for sharing these valuable lectures. Using them and reading Prof. Boyds' book, I not only could ponder the convex optimization concepts and dig through the problems that I am facing in my field (the electricity markets) but also learn how to make the course more attractive for the students even if its a completely serious mathematic course. Thank you, Professor Boyd.
1:03:00 Duality
1:13:45 What did Prof. meant by saying "for the max cut problem, it was proven that the bound was never more than 13% off?"
max cut is an optimization problem which could be modeled as SDP and get a approximation ratio of 0.878, i.e 13% off, see www2.cs.duke.edu/courses/fall15/compsci532/scribe_notes/lec17.pdf
At @20:14 how can the LMI be general ? A less than lambda I in the matrix sense needs to hold only for eigenvectors , why is it general?
Does anyone know any matlab tutorial for convex optimization?
Oops. Steve wrote min. on minute 25:02 . I thought he said that was a sloppy practice :)
Wilmer Henao "...note the period..."
He put dot at the end for abbreviation of minimize. He said that was totally ok in that video
7:07 SDP
27:37 vector optimization
How to show that “lamda_max(A)≤t" is equivalent to "t*I-A is positive semidefinite"?
Eigenvalues of t-IA is t - eigenvalue(A). So for all eigenvalue be positive (positive definite), lambda_max (A)
1:04:02
No... That is almost a textbook definition
沙发
wrong,idts. no such thing as interesx or not about it, or interesx thus hear more etc. ts just toolx not interex. ceptu thesex, any be any interesx no matter what, say/can say any no matter what, no such thing as tryx or etc
沙发