Skip to content
lectern.tv
Stanford EE364a: Convex Optimization I →0/22
1

Mathematical Optimization & Why Convexity Is the Watershed

1:20:32
2

Stanford EE364a Lecture 2: Affine Sets, Convex Sets, Cones & Hyperplanes

1:16:51
3

Stanford EE364a Lecture 3: Convex Functions — First/Second-Order Conditions & Epigraphs

1:17:14
4

Stanford EE364a Lecture 4: Convex Optimization Problems — LP, QP, SOCP & SDP

1:13:38
5

Stanford EE364a Lecture 5: The Lagrangian, Dual Function & Slater's Condition

1:16:10
6

Stanford EE364a Lecture 6: Saddle-Point Interpretation, KKT Conditions & Perturbation

1:09:19
7

Lecture 7 | Convex Optimization I

1:14:37
8

Lecture 8 | Convex Optimization I (Stanford)

1:16:30
9

Lecture 9: Duality Theory (EE364a)

1:16:36
10

Lecture 10: Approximation and Fitting (EE364a)

1:17:55
11

Lecture 11: Statistical Estimation (EE364a)

1:17:03
12

Lecture 12: Geometric Problems (EE364a)

1:16:05
13

Lecture 13: Geometric Problems Continued (EE364a)

1:15:17
14

Lecture 14: Numerical Linear Algebra Background (EE364a)

1:10:12
15

Lecture 15: Unconstrained Minimization (EE364a)

1:16:45
16

Lecture 16: Equality Constrained Minimization (EE364a)

1:13:59
17

Lecture 17: Equality Constrained Minimization Continued (EE364a)

1:19:25
18

Lecture 18: Interior-Point Methods (EE364a)

1:16:53
19

Lecture 19: Advanced Topics and Applications (EE364a)

1:15:02
20

Lecture 1: Subgradients (EE364B)

1:01:37
21

Lecture 2: Subgradients Continued (EE364B)

1:07:27
22

Lecture 3: Subgradient Methods (EE364B)

1:14:45

Lecture 11: Statistical Estimation (EE364a)

Stephen BoydStanford University

Professor

1:17:03·2008·graduate level

This lecture, part of Stanford's EE 364A, elucidates the application of statistical estimation techniques within the framework of convex optimization, detailing problem formulations and solution methodologies.

EngineeringConvex OptimizationSignal Processing & RoboticsStatistical EstimationConvex Analysis

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how statistical estimation can be used in convex optimization for the course, Convex Optimization I (EE 364A).

Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.

Previous · Lecture 10

Lecture 10: Approximation and Fitting (EE364a)

Next · Lecture 12

Lecture 12: Geometric Problems (EE364a)

Seminar notes & discussion

Sign in to comment

No public seminar notes on this lecture yet.