The universal functional optimization (UFO) system is an interactive modular system for solving both dense medium-size and sparse large-scale optimization problems. The UFO system can be used for the following applications: 1. Formulation and solution of particular optimization problems that are described in Chapter 2. 2. Preparation of specialized optimization routines (or subroutines) based on methods described in Chapter 3. 3. Designing and testing new optimization methods. The UFO system is a very useful tool for the development of optimization algorithms. The special realization of the UFO system described in the subsequent text makes this system portable and extensible and we continue with its further development.

References in zbMATH (referenced in 40 articles )

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  1. Dinc Yalcin, Gulcin; Kasimbeyli, Refail: Weak subgradient method for solving nonsmooth nonconvex optimization problems (2021)
  2. Mahdavi-Amiri, N.; Shaeiri, M.: A conjugate gradient sampling method for nonsmooth optimization (2020)
  3. Maleknia, Morteza; Shamsi, Mostafa: A gradient sampling method based on ideal direction for solving nonsmooth optimization problems (2020)
  4. Maleknia, M.; Shamsi, M.: A new method based on the proximal bundle idea and gradient sampling technique for minimizing nonsmooth convex functions (2020)
  5. Duintjer Tebbens, Jurjen; Matonoha, Ctirad; Matthios, Andreas; Papáček, Štěpán: On parameter estimation in an in vitro compartmental model for drug-induced enzyme production in pharmacotherapy. (2019)
  6. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  7. Kuřátko, Jan: Factorization of saddle-point matrices in dynamical systems optimization -- reusing pivots (2019)
  8. Lukšan, Ladislav; Vlček, Jan: A hybrid method for nonlinear least squares that uses quasi-Newton updates applied to an approximation of the Jacobian matrix. (2019)
  9. Vlček, Jan; Lukšan, Ladislav: Application of the infinitely many times repeated BNS update and conjugate directions to limited-memory optimization methods. (2019)
  10. Vlček, Jan; Lukšan, Ladislav: Properties of the block BFGS update and its application to the limited-memory block BNS method for unconstrained minimization (2019)
  11. Vlček, Jan; Lukšan, Ladislav: A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions (2019)
  12. Lukšan, Ladislav; Vlček, Jan: New quasi-Newton method for solving systems of nonlinear equations. (2017)
  13. Matonoha, Ctirad; Papáček, Štěpán; Kindermann, Stefan: On the optimization of initial conditions for a model parameter estimation. (2017)
  14. Papáček, Štěpán; Macdonald, Benn; Matonoha, Ctirad: Closed-form formulas vs. PDE based numerical solution for the FRAP data processing: theoretical and practical comparison (2017)
  15. Vlček, Jan; Lukšan, Ladislav: A generalized limited-memory BNS method based on the block BFGS update. (2017)
  16. Yousefpour, Rohollah: Combination of steepest descent and BFGS methods for nonconvex nonsmooth optimization (2016)
  17. Akbari, Z.; Yousefpour, R.; Reza Peyghami, M.: A new nonsmooth trust region algorithm for locally Lipschitz unconstrained optimization problems (2015)
  18. Curtis, Frank E.; Que, Xiaocun: A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees (2015)
  19. Kindermann, Stefan; Papáček, Štěpán: On data space selection and data processing for parameter identification in a reaction-diffusion model based on FRAP experiments (2015)
  20. Matonoha, C.; Papáček, Š.: On the connection and equivalence of two methods for solving an ill-posed inverse problem based on FRAP data (2015)

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