what course to do after convex optimization

What skills do you need to do convex optimization?

Her research applies convex optimization techniques to a variety of non-convex applications, including sigmoidal programming, biconvex optimization, and structured reinforcement learning problems, with applications to political science, biology, and operations research. You should have good knowledge of linear algebra and exposure to probability.

What are the topics in convex analysis?

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. Interiorpoint methods.

What happens after a course session ends?

After a course session ends, it will be archived . I would like to receive email from StanfordOnline and learn about other offerings related to Convex Optimization. This course concentrates on recognizing and solving convex optimization problems that arise in applications.

Why should an engineer study convex optimization?

By using the methods of convex optimization, we can solve linear and quadratic programs easily and efficiently. It can be used to figure out things like attainable performance. Convex optimization solves problems using tools like bundle methods, subgradient projection, and ellipsoid methods.

Is convex optimization difficult?

Nonconvex optimization problems are generally very difficult to solve, although there are some rare exceptions. m j=1 akj pj , k = 1,...,n. Applications: before 1990s: mostly in operations research; few in engineering since 1990: many new applications in engineering and new problem classes (SDP, SOCP, robust optim.)

What are the prerequisites for convex optimization?

The prerequisites for this course is introduction to linear algebra like introduction to the concepts like matrices, eigenvectors, symmetric matrices; basic calculus and introduction to the optimization like introduction to the concepts of linear programming.

Where is convex optimization used?

Convex optimization can be used to also optimize an algorithm which will increase the speed at which the algorithm converges to the solution. It can also be used to solve linear systems of equations rather than compute an exact answer to the system.

Is convex optimization important for deep learning?

The answer is No. You might want to argue that convex optimization shouldn't be that interesting for machine learning since we often encounter loss surfaces like image below, that are far from convex.

Why convex Optimisation is important?

Convexity in gradient descent optimization Our goal is to minimize this cost function in order to improve the accuracy of the model. MSE is a convex function (it is differentiable twice). This means there is no local minimum, but only the global minimum. Thus gradient descent would converge to the global minimum.

What are the prerequisites for optimization?

Prerequisites. Good knowledge of linear algebra (as in 22255), and exposure to probability. Exposure to numerical computing, optimization, and application fields helpful but not required; the applications will be kept basic and simple. You will use one of CVX (Matlab), CVXPY (Python), Convex.

Why is convex optimization used in machine learning?

Because the optimization process / finding the better solution over time, is the learning process for a computer. I want to talk more about why we are interested in convex functions. The reason is simple: convex optimizations are "easier to solve", and we have a lot of reliably algorithm to solve.

What is convex analysis used for?

Convex analysis is the branch of mathematics devoted to the study of properties of convex functions and convex sets, often with applications in convex minimization, a subdomain of optimization theory.

Why convex is important?

Convex functions play an important role in many areas of mathematics. They are especially important in the study of optimization problems where they are distinguished by a number of convenient properties. For instance, a strictly convex function on an open set has no more than one minimum.

Is linear programming convex optimization?

Linear programming is a special case of convex optimization where the objective function is linear and the constraints consist of linear equalities and inequalities. Nonlinear programming concerns optimization where at least one of the objective function and constraints is nonlinear.

Are neural networks convex optimization?

Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks.

Convex Optimization

This course concentrates on recognizing and solving convex optimization problems that arise in applications.

About this course

This course concentrates on recognizing and solving convex optimization problems that arise in applications.

What you'll learn

How to recognize convex optimization problems that arise in applications.

Frequently Asked Questions

No, the textbook is available online at http://www.stanford.edu/~boyd/cvxbook/.

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S.

Learning Objective

1) Understand basics of convex analysis and convex optimization problems.

Description

This course aims to introduce students basics of convex analysis and convex optimization problems, basic algorithms of convex optimization and their complexities, and applications of convex optimization in aerospace engineering.

Projects

The project will be decided with the instructor in the mid of the semester. it will be relevant to theory or application of convex optimization.

Textbooks

Official textbook information is now listed in the Schedule of Classes. NOTE: Textbook information is subject to be changed at any time at the discretion of the faculty member. If you have questions or concerns please contact the academic department.

Computer Requirements

Any O/S will be appropriate. Require at least one programming language, including byt not limited to Matlab, Python, C/C+, and Java.

Course Description

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.

Instructor

Stephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University. His current research focus is on convex optimization applications in control, signal processing, and circuit design.

Assignments

Unless otherwise noted, all reading assignments are from the textbook. Copyright in this book is held by Cambridge University Press.