minpack

Notes on optimization software. This paper is an attempt to indicate the current state of optimization software and the search directions which should be considered in the near future. There are two parts of this paper. In the first part I discuss some of the issues that are relevant to the development of general optimization software. I have tried to focus on those issues which do not seem to have received sufficient attention and which would significantly benefit from further research. In addition, I have chosen issues that are particularly relevant to the development of software for optimization libraries. In the second part I illustrate some of the points raised in the first part by discussing algorithms for unconstrained optimization. Because the discussion in this part is brief, the interested reader may want to consult other papers in this volume for further information. In both parts my comments are influenced by my involvement in the MINPACK project and by my experiences in the development of MINPACK-1 [cf. the author, B. S. Garbow and K. E. Hillstrom, ACM Trans. Math. Software 7, 17-41 (1981; Zbl 0454.65049)]. (Source: http://plato.asu.edu)


References in zbMATH (referenced in 740 articles , 2 standard articles )

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  1. Bozorgmanesh, Hassan; Hajarian, Masoud: Triangular decomposition of CP factors of a third-order tensor with application to solving nonlinear systems of equations (2022)
  2. Chen, Haotian: On locating the zeros and poles of a meromorphic function (2022)
  3. Danmalam, Kamaluddeen Umar; Mohammad, Hassan; Waziri, Mohammed Yusuf: Structured diagonal Gauss-Newton method for nonlinear least squares (2022)
  4. Gonçalves, M. L. N.; Lima, F. S.; Prudente, L. F.: A study of Liu-Storey conjugate gradient methods for vector optimization (2022)
  5. Grapiglia, Geovani N.; Stella, Gabriel F. D.: An adaptive trust-region method without function evaluations (2022)
  6. Grapiglia, G. N.; Gonçalves, M. L. N.; Silva, G. N.: A cubic regularization of Newton’s method with finite difference Hessian approximations (2022)
  7. Li, Xiangli; Zhao, Wenjuan: A modified conjugate gradient method based on the self-scaling memoryless BFGS update (2022)
  8. Luo, Xin-long; Xiao, Hang; Lv, Jia-hui: Continuation Newton methods with the residual trust-region time-stepping scheme for nonlinear equations (2022)
  9. Martínez, J. M.; Santos, L. T.: On large-scale unconstrained optimization and arbitrary regularization (2022)
  10. Wang, Qifeng; Li, Weiguo; Bao, Wendi; Gao, Xingqi: Nonlinear Kaczmarz algorithms and their convergence (2022)
  11. Abubakar, Auwal Bala; Kumam, Poom; Mohammad, Hassan; Ibrahim, Abdulkarim Hassan: PRP-like algorithm for monotone operator equations (2021)
  12. Abubakar, Auwal Bala; Muangchoo, Kanikar; Ibrahim, Abdulkarim Hassan; Abubakar, Jamilu; Rano, Sadiya Ali: FR-type algorithm for finding approximate solutions to nonlinear monotone operator equations (2021)
  13. Amagai, Kenji; Hatano, Yuko; Machida, Manabu: Linear transport in porous media (2021)
  14. Areias, P.; Rabczuk, T.: An engineering interpretation of Nesterov’s convex minimization algorithm and time integration: application to optimal fiber orientation (2021)
  15. Assunção, P. B.; Ferreira, O. P.; Prudente, L. F.: Conditional gradient method for multiobjective optimization (2021)
  16. Audet, Charles; Bigeon, Jean; Couderc, Romain: Combining cross-entropy and MADS methods for inequality constrained global optimization (2021)
  17. Awwal, Aliyu Muhammed; Mohammad, Hassan; Yahaya, Mahmoud Muhammad; Muhammadu, Ahmadu Bappah; Ishaku, Adamu: A new algorithm with structured diagonal Hessian approximation for solving nonlinear least squares problems and application to robotic motion control (2021)
  18. Birgin, E. G.; Martínez, J. M.; Ramos, A.: On constrained optimization with nonconvex regularization (2021)
  19. Braden, Jonathan; Burrage, Clare; Elder, Benjamin; Saadeh, Daniela: (\varphi)\textttenics: Vainshtein screening with the finite element method (2021)
  20. Burachik, Regina S.; Caldwell, Bethany I.; Kaya, C. Yalçın: A generalized multivariable Newton method (2021)

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