首页

投稿

斯坦福大学【精译⚡凸优化 Convex Optimization 2020】

1.3万

3

2025-02-19 19:00:04

-

最小二乘法

新学期多点新知识

常青藤中英字幕课程发消息

【唯一翻译发布账号，站内盗搬号/仿制号较多，大家注意甄别】，字幕质量资料请评论区，课程信息见投稿简介，求课先看合集。

【凸优化 EE364A 2023】斯坦福大学—中英字幕

【凸优化 EE364A 2008】斯坦福大学—中英字幕

【凸优化 10-725 2018】卡耐基梅隆—中英字幕

p1 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p01 Lectur

01:16:57

p2 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p02 Lectur

01:16:36

p3 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p03 Lectur

01:20:28

p5 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p05 Lectur

01:20:54

p6 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p06 Lectur

01:13:55

p7 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p07 Lectur

01:15:11

p8 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p08 Lectur

01:16:32

p9 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p09 Lectur

01:22:42

p10 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p10 Lect

01:14:01

p11 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p11 Lect

01:15:00

p12 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p12 Lect

01:19:08

p13 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p13 Lect

01:17:34

p14 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p14 Lect

01:18:38

p15 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p15 Lect

01:09:33

p16 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p16 Lect

01:15:55

p17 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p17 Lect

01:20:05

p18 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p18 Lect

01:14:57

p19 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p19 Lect

01:17:52

p20 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p20 Lect

01:15:17

p21 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p21 Lect

01:14:51

p23 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p23 Lect

01:17:15

p24 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p24 Lect

01:16:45

p25 卡耐基梅隆大学 10-725 凸优化 Convex Optimization（Fall 2018） p25 Lect

01:14:13

【线性优化 CS525 2021】威斯康星大学—中英字幕

p1 Linear Optimization - Video 1： Variants of the linear programming problem

57:13

p2 Linear Optimization - Video 2： Examples of LP problems

33:25

p3 Linear Optimization - Video 3： Piecewise linear convex functions

15:09

p4 Linear Optimization - Video 4： Graphical representation and solution

23:39

p5 Linear Optimization - Video 5： Polyhedra and convex sets

14:35

p6 Linear Optimization - Video 6： Extreme points, vertices, and basic feasible s

48:04

p7 Linear Optimization - Video 7： Polyhedra in standard form

30:16

p8 Linear Optimization - Video 8： Degeneracy

18:12

p9 Linear Optimization - Video 9： Existence of extreme points

13:03

p10 Linear Optimization - Video 10： Optimality of extreme points

18:49

p11 Linear Optimization - Video 11： Representation of polytopes

05:42

p12 Linear Optimization - Video 12： Projections of polyhedra： Fourier-Motzkin el

25:02

p13 Linear Optimization - Video 13： Optimality conditions

42:01

p14 Linear Optimization - Video 14： Development of the simplex method

49:30

p15 Linear Optimization - Video 15： Algorithms and operation counts

20:43

p16 Linear Optimization - Video 16： Implementations of the simplex method

01:32:12

p17 Linear Optimization - Video 17： Anticycling： lexicography and Bland's rule

41:18

p18 Linear Optimization - Video 18： Finding an initial basic feasible solution

37:19

p19 Linear Optimization - Video 19： Column geometry and the simplex method

19:14

p20 Linear Optimization - Video 20： Computational efficiency of the simplex meth

27:32

p21 Linear Optimization - Video 21： Motivation

18:26

p22 Linear Optimization - Video 22： The dual problem

24:53

p23 Linear Optimization - Video 23： The duality theorem

47:40

p24 Linear Optimization - Video 24： Optimal dual variables as marginal costs

07:11

p25 Linear Optimization - Video 25： Standard form problems and the dual simplex

32:19

p26 Linear Optimization - Video 26： Farkas' lemma and linear inequalities

11:38

p27 Linear Optimization - Video 27： Graphs

27:39

p28 Linear Optimization - Video 28： Formulation of the network flow problem

20:33

p29 Linear Optimization - Video 29： The network simplex algorithm

01:21:15

p30 Linear Optimization - Video 30： Efficient algorithms and computational compl

14:31

p31 Linear Optimization - Video 31： The key geometric result behind the ellipsoi

08:11

p32 Linear Optimization - Video 32： The ellipsoid method for the feasibility pro

28:41

p33 Linear Optimization - Video 33： The ellipsoid method for optimization

10:23

p34 Linear Optimization - Video 34： Problems with exponentially many constraints

19:15

【凸优化 2020】斯坦福大学

【精译⚡随机方法与优化】卡耐基梅隆大学•10-801

Ahmad Bazzi【中英⚡凸优化|Convex Optimization】

P1 Convex Optimization ｜ Introduction by Dr. Ahmad Bazzi

48:18

P2 Convex Sets ｜ Convex Optimization by Dr. Ahmad Bazzi

02:08:02

P3 Convex Functions ｜ Convex Optimization by Dr. Ahmad Bazzi

01:23:06

P4 Convex Optimization Principles ｜ Convex Optimization by Dr. Ahmad Bazzi

37:04

P5 Linear Programming & SIMPLEX algorithm w MATLAB ｜ Convex Optimization

02:04:01

P6 Quadratic Programs ｜ Convex Optimization by Dr. Ahmad Bazzi

26:13

P7 Quadratically Constrained Quadratic Programs ｜ Convex Optimization by Dr. Ahm

43:52

P8 Second Order Cone Programming (SOCP) ｜ Convex Optimization by Dr. Ahmad Bazzi

01:04:46

P9 Geometric Programs (GP) ｜ Convex Optimization by Dr. Ahmad Bazzi

39:23

P10 Generalized Geometric Programs (GGP) ｜ Convex Optimization by Dr. Ahmad Bazz

40:14

P11 Semidefinite Programming (SDP) ｜ Convex Optimization by Dr. Ahmad Bazzi

36:50

P12 Vector and Multicriterion Optimization ｜ Pareto Optimal points and the Paret

01:13:57

P13 Optimal Trade-off Analysis ｜ Convex Optimization by Dr. Ahmad Bazzi

13:02

P14 Lagrange Dual Function ｜ Convex Optimization by Dr. Ahmad Bazzi

37:47

P15 Lagrange Dual Problem ｜ Convex Optimization by Dr. Ahmad Bazzi

01:13:58

P16 Certificate of Suboptimality (ε-suboptimality) ｜ Convex Optimization by Dr.

18:39

P18 Complementary Slackness ｜ Convex Optimization by Dr. Ahmad Bazzi

17:06

P20 KKT Conditions ｜ Convex Optimization by Dr. Ahmad Bazzi

11:45

P21 Perturbation and Sensitivity Analysis ｜ Convex Optimization by Dr. Ahmad Baz

57:33

P22 Equivalent Reformulations ｜ Convex Optimization by Dr. Ahmad Bazzi

01:34:09

P23 Weak Alternatives ｜ Convex Optimization by Dr. Ahmad Bazzi

23:43

P24 Strong Alternatives ｜ Convex Optimization by Dr. Ahmad Bazzi

37:36

P25 Strong Alternatives ｜ Convex Optimization by Dr. Ahmad Bazzi

39:12

P26 Descent, Backtracking & Unconstrained Minimization ｜ Convex Optimization by

01:32:50

哈佛大学【中英⚡数值分析|Math205 2021 Applied Math】

斯坦福大学【中英⚡凸优化II|EE364B 2020Convex Optimization II】

斯坦福大学【中英⚡应用线性代数导论|ENGR108 2020 Introduction to Applied Linear Algebra】

P1 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 1 - Introduction

10:26

P2 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 2 - vector notation

20:00

P3 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 3 - vector examples

18:24

P4 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 4-addition & scalar mult.

27:52

P5 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 5 - VMLS inner product

14:05

P6 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 6 - VMLS complexity

06:16

P7 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 7 - VMLS linear functions

13:59

P8 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 8-VMLS taylor approx & reg

20:49

P9 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 9 - VMLS norm

15:05

P10 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 10 - VMLS distance

14:15

P11 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 11-VMLS std. deviation

16:15

P12 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 12 - VMLS angle

23:39

P13 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 13 - VMLS k means

31:38

P14 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 14-VMLS k means app.

19:10

P15 to Applied Linear Algebra ｜ 2020 ｜ Lecture 15-VMLS linear ind.

25:09

P16 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 16-VMLS Gram Schmidt algo

15:24

P17 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 17 - VMLS matrix notation

42:25

P18 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 18-VMLS matrix vector mul

08:10

P19 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 19-VMLS matrix vector ex

28:10

P20 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 20-VMLS selector matrices

06:04

P21 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 21 - VMLS incidence matri

15:42

P22 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 22 - VMLS convolution

16:10

P23 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 23-VMLS vector linear fun

15:59

P24 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 24-VMLS linear func model

17:26

P25 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 25 - VMLS linear equation

22:43

P26 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 26-VMLS linear dynamic sy

39:25

P27 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 27-VMLS matrix mult

25:25

P28 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 28-VMLS matrix mult ex

08:18

P29 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 29-VMLS mtrx pwrs & QR fa

16:01

P30 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 30-VMLS left & right inv.

35:22

P31 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 31-VMLS solving linear eq

20:02

P32 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 32 - VMLS pseudo inverse

14:26

P33 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 33 - VMLS least squares

28:07

P34 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 34-VMLS least squares ex.

22:25

P35 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 35-VMLS LS data fitting

15:11

P36 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 36-VMLS fit univariate fn

38:02

P37 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 37 - VMLS validation

30:38

P38 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 38 - VMLS classification

19:01

P39 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 39-VMLS LS classification

16:46

P40 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 40-VMLS multiclass classi

19:41

P41 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 41-VMLS multi objective L

21:25

P42 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 42-ctrl via multi obj LS

12:05

P43 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 43-MLS est & inversion

23:51

P44 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 44-VMLS reg data fitting

14:15

P45 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 45-VMLS constrained LS

22:03

P46 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 46-VMLS solve cstr LS pro

16:44

P47 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 47-VMLS portfolio optim

49:48

P48 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 48-VMLS linear quadrt ctr

23:55

P49 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 49-VMLS lin quadrt st est

15:45

P50 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 50-VMLS nonlinear eq. & L

20:01

P51 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 51-VMLS Leven. Marq. algo

20:02

P52 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 52-VMLS nonlin mdl fittin

15:57

P53 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 53-VMLS cstrd nonlinear L

17:13

P54 duction to Applied Linear Algebra ｜ 2020 ｜ Lecture 54-VMLS aug Lagragian mth

21:43

普林斯顿【中英⚡机器学习优化|2021 COS512 Optimization for Machine Learning】

卡内基梅隆大学【中英⚡凸优化|Fall 2018, Convex Optimization】

普渡大学【中英⚡计算优化|ChE597 Spring 2025, Computational Optimization】

卡内基梅隆大学【中英⚡凸优化|10-725 Fall 2016, Convex Optimization】

加州大学伯克利分校【中英⚡工程优化模型|EECS 227A Fall 2022, Optimization Models in Engineering】

豪斯多夫数学研究所【中英⚡组合优化与算法前沿系列讲座|组合优化与算法前沿系列讲座】

西蒙研究所【中英⚡凸优化|Online Learning & Convex Optimization】

印度理工学院【中英⚡线性规划与组合优化|CS5210 Spring 2022, Linear Programming and Combinatorial Op】

顶部