Skip to main content
King Abdullah University of Science and Technology
Numerical Methods for PDEs
NumPDE
Numerical Methods for PDEs

Main navigation

  • Home
  • People
    • All Profiles
    • Principal Investigators
    • Postdoctoral Fellows
    • Students
    • Former Members
  • Events
    • All Events
    • Events Calendar
  • News
  • About
  • Activities
  • Slides
  • NumPDE Workshop 2025
  • CAMWA 50
  • POEMS 2026

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.

Numerical Methods for PDEs (NumPDE)

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