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SUMMARY:Clarabel: An Interior Point Solver for Quadratic Conic Optimizatio
 n (new room ME C2 405)
DTSTART:20230629T150000
DTEND:20230629T160000
DTSTAMP:20260511T050405Z
UID:4ff307df5214135e6ed2c4705270bf3e1abbcce8adbf5ebe5d292eaa
CATEGORIES:Conferences - Seminars
DESCRIPTION:Yuwen Chen (University of Oxford\, England)\nAbstract: Convex
  optimization finds applications in various fields\, such as machine learn
 ing\, signal processing\, control systems\, finance\, logistics\, and oper
 ations research\, and relies on numerical software to solve problems effic
 iently. We are going to introduce our numerical solver called Clarabel\,
  which is based on an interior point method with homogeneous embedding. It
  supports a variety of conic constraints beyond linear programming (LP) an
 d quadratic programming (QP)\, including second-order cones\, semidefinite
  cones\, exponential cones\, and power cones. The solver was originally de
 veloped in Julia and Rust\, but we also provide support for Python. It per
 forms faster and more stably than state-of-the-art solvers on a class of c
 onic optimization problems.\n\n \n\nBio: Yuwen Chen holds a B.Sc. degree
  in Electrical Engineering from Shanghai Jiao Tong University (China\, 201
 7) and an M.Sc. degree from the Department of Information Technology and E
 lectrical Engineering at ETH Zurich (Switzerland\, 2020). He is currently 
 a third-year Ph.D. student under the supervision of Prof. Paul Goulart in 
 the Department of Engineering Science at the University of Oxford. His res
 earch focuses on optimization algorithms and software development for conv
 ex optimization.
LOCATION:ME C2 405 https://plan.epfl.ch/?room==ME%20C2%20405
STATUS:CONFIRMED
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