Convex optimization is an important tool in many areas of engineering. Recently we have seen convex optimization being used in an on-line setting where computational power and time is limited. In this case the optimization algorithm can usually only be obtained as a handtailored solution by convex optimization experts. This project will bring forth algorithms and software that automate this process such that a greater range of engineers and scientists can easily use real-time convex optimization. The proposed algorithms will, compared to state-of-the-art, efficiently use the possibilities of modern parallel and embedded computing platforms. This unleashes convex optimization models developed in the last 15 years for use in real-time signal processing systems.
Effective start/end date01/06/201430/06/2017

Research outputs

Collaborative partners

  • Aalborg University (lead)
  • University of Freiburg (Project partner)
  • Apple
  • University of California at Los Angeles
ID: 214682226