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Fernando Agustin Pazos
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2020 – today
- 2020
- [j7]Fernando Agustin Pazos
, Anibal Zanini
Performance Improvement of Discrete-time Linear Control Systems Subject to Varying Sampling Rates Using the Tikhonov Regularization Method. Int. J. Autom. Comput. 17(3): 453-463 (2020)
2010 – 2019
- 2019
- [j6]Fernando Agustin Pazos, Amit Bhaya:
Matrix forms of iterative algorithms to solve large-scale discrete ill-posed problems with an application to image restoration. J. Appl. Math. Comput. 60(1-2): 113-145 (2019) - 2017
- [j5]Amit Bhaya
Cooperative concurrent asynchronous computation of the solution of symmetric linear systems. Numer. Algorithms 75(3): 587-617 (2017) - 2015
- [j4]Fernando Agustin Pazos, Amit Bhaya
Adaptive choice of the Tikhonov regularization parameter to solve ill-posed linear algebraic equations via Liapunov Optimizing Control. J. Comput. Appl. Math. 279: 123-132 (2015) - 2014
- [j3]Fernando Agustin Pazos, Amit Bhaya:
A control Liapunov function approach to generalized and regularized descent methods for zero finding. Int. J. Hybrid Intell. Syst. 11(2): 109-123 (2014) - 2012
- [j2]Fernando Agustin Pazos, Amit Bhaya
Design of second order neural networks as dynamical control systems that aim to minimize nonconvex scalar functions. Neurocomputing 97: 174-191 (2012) - [c8]Amit Bhaya
A cooperative conjugate gradient method for linear systems permitting multithread implementation of low complexity. CDC 2012: 638-643 - 2011
- [c7]Fernando Agustin Pazos, Amit Bhaya
Generalized Levenberg-Marquardt neural nets for minimization of quasiconvex scalar functions. NaBIC 2011: 231-236 - 2010
- [c6]Amit Bhaya
Cooperative parallel asynchronous computation of the solution of symmetric linear systems. CDC 2010: 774-779 - [c5]Amit Bhaya
The controlled conjugate gradient type trajectory-following neural net for minimization of nonconvex functions. IJCNN 2010: 1-8
2000 – 2009
- 2009
- [j1]Fernando Agustin Pazos, Amit Bhaya
Control Liapunov function design of neural networks that solve convex optimization and variational inequality problems. Neurocomputing 72(16-18): 3863-3872 (2009) - [c4]Amit Bhaya
Control-theoretic design of iterative methods for symmetric linear systems of equations. CDC 2009: 115-120 - [c3]Amit Bhaya
Comparative Study of the CG and HBF ODEs Used in the Global Minimization of Nonconvex Functions. ICANN (1) 2009: 668-677 - [c2]Fernando Agustin Pazos, Amit Bhaya
Unified control Liapunov function based design of neural networks that aim at global minimization of nonconvex functions. IJCNN 2009: 2467-2474 - 2007
- [c1]Fernando Agustin Pazos, Amit Bhaya
Algorithm for variational inequality problems based on a gradient dynamical system designed using a control Liapunov function. ISIC 2007: 596-601
Coauthor Index
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