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 15 articles )

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  1. Yousefpour, Rohollah: Combination of steepest descent and BFGS methods for nonconvex nonsmooth optimization (2016)
  2. Akbari, Z.; Yousefpour, R.; Reza Peyghami, M.: A new nonsmooth trust region algorithm for locally Lipschitz unconstrained optimization problems (2015)
  3. Curtis, Frank E.; Que, Xiaocun: A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees (2015)
  4. Matonoha, C.; Papáček, Š.: On the connection and equivalence of two methods for solving an ill-posed inverse problem based on FRAP data (2015)
  5. Lukšan, Ladislav; Vlček, Jan: Efficient tridiagonal preconditioner for the matrix-free truncated Newton method (2014)
  6. Královcová, Jiřina; Lukšan, Ladislav; Mlýnek, Jaroslav: Heat exposure optimization applied to moulding process in the automotive industry. (2013)
  7. Mäkelä, Marko M.; Karmitsa, Napsu; Bagirov, Adil: Subgradient and bundle methods for nonsmooth optimization (2013)
  8. Papáček, Štěpán; Kaňa, Radek; Matonoha, Ctirad: Estimation of diffusivity of phycobilisomes on thylakoid membrane based on spatio-temporal FRAP images (2013)
  9. Duintjer Tebbens, Jurjen; Tůma, Miroslav: Preconditioner updates for solving sequences of linear systems in matrix-free environment (2010)
  10. Hartman, Jan; Lukšan, Ladislav; Zítko, Jan: Automatic differentiation and its program realization (2009)
  11. Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan: Primal interior-point method for large sparse minimax optimization (2009)
  12. Tebbens, Jurjen Duintjer; Tůma, Miroslav: Efficient preconditioning of sequences of nonsymmetric linear systems (2007)
  13. Lukšan, Ladislav; Vlček, Jan: Numerical experience with iterative methods for equality constrained nonlinear programming problems (2001)
  14. Lukšan, Ladislav; Spedicato, Emilio: Variable metric methods for unconstrained optimization and nonlinear least squares (2000)
  15. Lukšan, L.: Hybrid methods for large sparse nonlinear least squares (1996)