Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Reports, Documentation, and Standards
  4. Optimization Notes
 
report

Optimization Notes

Kashani, Sepand  
December 16, 2019

While optimization is well studied for real-valued functions $f : \mathbb{R}^{N} \to \mathbb{R}$, many physical problems are (partially) specified in terms of complex-valued functions $f_{c} : \mathbb{C}^{N} \to \mathbb{C}^{M}$. Current optimization packages have limited support for such functions. In particular it is unclear how to define algorithmic differentiation w.r.t. complex-valued functions and arguments. This document is a collection of working notes on the topic.

  • Files
  • Details
  • Metrics
Type
report
Author(s)
Kashani, Sepand  
Date Issued

2019-12-16

Total of pages

6

Subjects

First-order Methods

•

Algorithmic Differentiation

Editorial or Peer reviewed

NON-REVIEWED

Written at

EPFL

EPFL units
LCAV  
RelationURL/DOI

IsNewVersionOf

https://infoscience.epfl.ch/record/270391
Available on Infoscience
December 17, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/164057
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés