convex optimization from stanford

视频来源:
https://www.youtube.com/watch?v=XFKBNJ14UmY
课程来源:
http://www.stat.cmu.edu/~ryantibs/convexopt/

Lecture 1 Introduction
1) 15:20
least squares is easier to solve than least absolute deviations, because it is smooth while the other is not

2) 28:00
第一部分是确定的像素，第二部分total variation.

3) 1:04:22

Lecture 2 Convexity I: Sets and functions
1) 26:20
norm cone: 对于内部点, norm cone为0向量, 对于边界点, norm cone 就是法向量相夹的cone, 如果只有一个法向量, 则该法向量为norm cone.

2) 46:46
Operations preserving convexity:
Scaling and translation中a为标量
Affine images and preimages中 A 为矩阵

3) 1:01:25

Lecture 3 Convexity II: Optimization basics
1) 6:23

2) 30:05