Convex optimization boyd course

  • How do you solve convex optimization?

    Unconstrained convex optimization can be easily solved with gradient descent (a special case of steepest descent) or Newton's method, combined with line search for an appropriate step size; these can be mathematically proven to converge quickly, especially the latter method..

  • What are the prerequisites for convex optimization?

    Prerequisites.
    Students should be familiar with linear algebra (e.g., solving systems of equations, matrix factorizations including SVD, QR, LU, Cholesky, least squares), basic probability (e.g.,\\ you should be comfortable with multivariate probability densities), and have good programming skills..

  • What is CVX in Matlab?

    \xb6 CVX is a modeling system for constructing and solving disciplined convex programs (DCPs)..

  • What is the syllabus of convex optimization?

    The syllabus includes: 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 .

Catalog description. Concentrates on recognizing and solving convex optimization problems that arise in applications. Convex sets, functions, and optimization  Lecture SlidesBoyd and VandenbergheShort course
Concentrates on recognizing and solving convex optimization problems that arise in applications. Convex sets, functions, and optimization problems. Basics of  Lecture SlidesBoyd and VandenbergheShort course

What are the course requirements for convex optimization?

Course requirements include a substantial project

Continuation of Convex Optimization I

Subgradient, cutting-plane, and ellipsoid methods

Decentralized convex optimization via primal and dual decomposition

Alternating projections

Exploiting problem structure in implementation


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