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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
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
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)
Stanford University offers the Convex Optimization online course through edX.
Yes. The course demands that the applicants have basic knowledge about linear algebra, probability, numerical computing, optimization, and familiarity with Matlab and CVX applications.
The topics covered in the syllabus are advanced in terms of difficulty.
If you study 10-15 hours a week, you will take eight weeks to complete the program.
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/.