Skip to main content
King Abdullah University of Science and Technology
Extensions of Dynamic Programming, Machine Learning, Discrete Optimization
TREES
Extensions of Dynamic Programming, Machine Learning, Discrete Optimization

Main navigation

  • Home
  • People
    • All Profiles
    • Principal Investigators
    • Postdoctoral Fellows
    • Students
    • Alumni
    • Former Members
  • Events
    • All Events
    • Events Calendar
  • News
  • Teaching
  • Collaborators
  • Books
  • Contact Us

semidefinite programming

A Storage-Optimal Convex Optimization Framework with Applications to Semidefinite Programming

Alp Yurtsever, PhD Candidate, EPFL

May 6, 12:00 - 13:00

B9 L2 H2

semidefinite programming convex optimization

With the ever-growing data sizes along with the increasing complexity of the modern problem formulations, there is a recent trend where heuristic approaches with unverifiable assumptions are overtaking more rigorous, conventional optimization methods at the expense of robustness. This trend can be overturned when we exploit dimensionality reduction at the core of optimization. I contend that even the classical convex optimization did not reach yet its limits of scalability.

Extensions of Dynamic Programming, Machine Learning, Discrete Optimization (TREES)

Footer

  • A-Z Directory
    • All Content
    • Browse Related Sites
  • Site Management
    • Log in

© 2025 King Abdullah University of Science and Technology. All rights reserved. Privacy Notice