SwePub
Sök i SwePub databas

  Utökad sökning

Träfflista för sökning "WFRF:(Amini Massoud) "

Sökning: WFRF:(Amini Massoud)

  • Resultat 1-3 av 3
Sortera/gruppera träfflistan
   
NumreringReferensOmslagsbildHitta
1.
  • 2021
  • swepub:Mat__t
  •  
2.
  • Alaghmandan, Mahmood, 1983, et al. (författare)
  • Dual Space and Hyperdimension of Compact Hypergroups
  • 2017
  • Ingår i: Glasgow Mathematical Journal. - 0017-0895 .- 1469-509X. ; 59:2, s. 421-435
  • Tidskriftsartikel (refereegranskat)abstract
    • We characterize dual spaces and compute hyperdimensions of irreducible representations for two classes of compact hypergroups namely conjugacy classes of compact groups and compact hypergroups constructed by joining compact and finite hypergroups. Also, studying the representation theory of finite hypergroups, we highlight some interesting differences and similarities between the representation theories of finite hypergroups and finite groups. Finally, we compute the Heisenberg inequality for compact hypergroups.
  •  
3.
  • Malek Mohammadi, Mohammareza, et al. (författare)
  • Recovery of Low-Rank Matrices Under Affine Constraints via a Smoothed Rank Function
  • 2014
  • Ingår i: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 62:4, s. 981-992
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, the problem of matrix rank minimization under affine constraints is addressed. The state-of-the-art algorithms can recover matrices with a rank much less than what is sufficient for the uniqueness of the solution of this optimization problem. We propose an algorithm based on a smooth approximation of the rank function, which practically improves recovery limits on the rank of the solution. This approximation leads to a non-convex program; thus, to avoid getting trapped in local solutions, we use the following scheme. Initially, a rough approximation of the rank function subject to the affine constraints is optimized. As the algorithm proceeds, finer approximations of the rank are optimized and the solver is initialized with the solution of the previous approximation until reaching the desired accuracy. On the theoretical side, benefiting from the spherical section property, we will show that the sequence of the solutions of the approximating programs converges to the minimum rank solution. On the experimental side, it will be shown that the proposed algorithm, termed SRF standing for smoothed rank function, can recover matrices, which are unique solutions of the rank minimization problem and yet not recoverable by nuclear norm minimization. Furthermore, it will be demonstrated that, in completing partially observed matrices, the accuracy of SRF is considerably and consistently better than some famous algorithms when the number of revealed entries is close to the minimum number of parameters that uniquely represent a low-rank matrix.
  •  
Skapa referenser, mejla, bekava och länka
  • Resultat 1-3 av 3

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy