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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

The Convex Optimization program will prove beneficial for students or professionals in electrical, mechanical, or civil engineering, computer science, scientific computing, Aero and Astro, operations research, and computational mathematics. This advanced-level course is excellent in learning how to solve application-oriented problems in the field of Convex Optimisation.

The Convex Optimization by edX covers a wide range of topics including, convex analysis, convex sets, linear and quadratic problems, duality theory, minimax, interior-point methods, statistics and machine learning, finance, digital and analog circuits. The course will benefit candidates looking to pursue a career in computer applications or machine learning.

The renowned Stanford University offers the eight-week Convex Optimization online course. There are video lectures in English and transcripts (also in English) to help you follow along. The course instructors, namely Stephen Boyd and Henryk Blasinski, are highly experienced in computer science, mathematical engineering, and other relevant disciplines. Several Ph.D. candidates will also mentor you as additional instructors.

The Highlights

Free learning
Advanced-level course
Experienced faculty
Video lectures in English
Transcripts in English
Stanford University program
Eight weeks, ten-fifteen hours each week
Self-paced learning

Programme Offerings

Free learning
video lectures
advanced-level course
Eight weeks
ten-fifteen hours each week
experienced faculty
Self-paced learning
Stanford University program

Courses and Certificate Fees

Certificate Availability
no

Eligibility Criteria

Learners interested in the Convex Optimization program must have relevant knowledge in the application of linear algebra, numerical computing, probability, and optimization. Candidates will also use Matlab and CVX to write scripts and so a basic understanding of these applications is expected.

What you will learn

Statistical skillsKnowledge of engineering

On completing the Convex Optimization certification syllabus, you will know the following:

Recognizing convex optimization problems in applications
Understanding of how these problems are solved
Understanding the background necessary to use the convex optimization methods in applications or research
Presenting the theory behind the convex optimization problems and arriving at results

Who it is for

Data AnalystData Scientist

The Convex Optimization online course is suitable for both students and professionals, interested in learning more about convex optimization problems and how to solve them.

Admission Details

Candidates interested in the Convex Optimization program must register for it as given below:

Step 1. Visit the official edX website: https://www.edx.org/

Step 2. Type “Convex Optimization” in the search bar and select the course from the search results.

Step 3. Find the “Enroll” button and click on it to proceed to the account creation page.

Step 4. Now, you can use your existing Facebook, Google, Microsoft, or Apple ID as your login credentials, or create a new account. Once this the done, click on Submit.

Step 5. Post your account creation, you will get a verification email, and upon verification, you can start studying.

Application Details

Candidates looking to pursue the Convex Optimization training need not fill any application form. However, all candidates must register on the edX platform by creating a new account by giving their name, country of origin, email id, and password or log in directly using their Facebook, Apple, Google, or Microsoft ids, to start the course successfully.

The Syllabus

Least-squares and linear programming
Mathematical optimization
Convex optimization
Course goals and topics
Example
Convex optimization: Brief history
Nonlinear optimization
Homework

Survey (Pre-Course)

Some important examples
Affine and convex sets
Generalized inequalities
Operations to preserve convexity
Dual cones and Generalised inequalities
Separate and support hyperplanes
Homework

Operations that preserve
Convexity and Generalised inequalities
Basic properties and examples
Quasiconvex functions
The conjugate function
Log-convex and Log-concave functions
Homework

Convex optimization problems
The optimization problem in standard form
Linear optimization
Quadratic optimization
Quasiconvex optimisation
Generalized inequality constraints
Geometric programming
Vector optimization
Semidefinite programming
Homework

Introduction
Weak and strong
Lagrange dual problem
Optimality conditions
Geometric interpretation
Examples
Perturbation and sensitivity analysis
Generalized inequalities
Homework

Introduction
Least-norm problems
Norm approximation
Robust approximation
Regularised approximation
Homework

Experiment design
Optimal detector design
Maximum likelihood estimation
Homework

Centering
Classification
Extremal volume ellipsoids
Homework

Introduction
Algorithm complexity and Matrix structure
Solve linear equations with factored matrices
Block elimination and the matrix inversion lemma
LU, Cholesky LDL factorization
Homework

Gradient descent method
Terminology and assumptions
Newton's method
Steepest descent method
Implementation
Self-concordant functions
Homework

Eliminating equality constraints
Implementation
Equality constrained minimization
Infeasible start Newton method
Newton's method with equality constraints
Homework

Logarithmic barrier function and central path
Inequality constrained minimization
Feasibility and phase I methods
Barrier method
Generalized inequalities
Complexity analysis through self-concordance
Homework

Post Course Survey
Main ideas of the course

Problems

Demo video

Instructors

Stanford Frequently Asked Questions (FAQ's)

1: The Convex Optimization course is offered by which University?

Stanford University offers the Convex Optimization online course through edX.

2: Are there any prerequisites to the course I should adhere to?

Yes. The course demands that the applicants have basic knowledge about linear algebra, probability, numerical computing, optimization, and familiarity with Matlab and CVX applications.

3: What is the level of difficulty in the course?

The topics covered in the syllabus are advanced in terms of difficulty.

4: What is the total time I would take to finish the course successfully?

If you study 10-15 hours a week, you will take eight weeks to complete the program.

5: Should I acquire a Matlab license, or will I be provided with it during the course?

A Matlab license will not be provided during the course. You can get the trial version or choose between different licensed versions at https://www.mathworks.com/.