minFunc is a Matlab function for unconstrained optimization of differentiable real-valued multivariate functions using line-search methods. It uses an interface very similar to the Matlab Optimization Toolbox function fminunc, and can be called as a replacement for this function. On many problems, minFunc requires fewer function evaluations to converge than fminunc (or minimize.m). Further it can optimize problems with a much larger number of variables (fminunc is restricted to several thousand variables), and uses a line search that is robust to several common function pathologies.

References in zbMATH (referenced in 21 articles )

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  1. Jensen, Henrik G.; Lauze, François; Darkner, Sune: Information-theoretic registration with explicit reorientation of diffusion-weighted images (2022)
  2. Li, Donghui; Wang, Xiaozhou; Huang, Jiajian: Diagonal BFGS updates and applications to the limited memory BFGS method (2022)
  3. Han, Chao; Lei, Yu; Xie, Yu; Zhou, Deyun; Gong, Maoguo: Learning smooth representations with generalized softmax for unsupervised domain adaptation (2021)
  4. An, Congpei; Xiao, Yuchen: Numerical construction of spherical (t)-designs by Barzilai-Borwein method (2020)
  5. Ahmed, Ali; Aghasi, Alireza; Hand, Paul: Simultaneous phase retrieval and blind deconvolution via convex programming (2019)
  6. Derkach, Dmytro; Ruiz, Adria; Sukno, Federico M.: Tensor decomposition and non-linear manifold modeling for 3D head pose estimation (2019)
  7. Driggs, Derek; Becker, Stephen; Aravkin, Aleksandr: Adapting regularized low-rank models for parallel architectures (2019)
  8. She, Yiyuan; Tang, Shao: Iterative proportional scaling revisited: a modern optimization perspective (2019)
  9. Gao, Wenbo; Goldfarb, Donald: Block BFGS methods (2018)
  10. Brockmeier, Austin J.; Mu, Tingting; Ananiadou, Sophia; Goulermas, John Y.: Quantifying the informativeness of similarity measurements (2017)
  11. Hamdi, Hamidreza; Couckuyt, Ivo; Sousa, Mario Costa; Dhaene, Tom: Gaussian processes for history-matching: application to an unconventional gas reservoir (2017)
  12. Schmidt, Mark; Le Roux, Nicolas; Bach, Francis: Minimizing finite sums with the stochastic average gradient (2017)
  13. Artioli, E.; Bisegna, P.: An incremental energy minimization state update algorithm for 3D phenomenological internal-variable SMA constitutive models based on isotropic flow potentials (2016)
  14. Bogosel, Beniamin; Oudet, Édouard: Qualitative and numerical analysis of a spectral problem with perimeter constraint (2016)
  15. Hirayama, Jun-ichiro; Hyvärinen, Aapo; Ishii, Shin: Sparse and low-rank matrix regularization for learning time-varying Markov networks (2016)
  16. Lazar, Markus; Jarre, Florian: Calibration by optimization without using derivatives (2016)
  17. Vaksman, Gregory; Zibulevsky, Michael; Elad, Michael: Patch ordering as a regularization for inverse problems in image processing (2016)
  18. Brockmeier, Austin J.; Choi, John S.; Kriminger, Evan G.; Francis, Joseph T.; Principe, Jose C.: Neural decoding with kernel-based metric learning (2014)
  19. du Plessis, Marthinus Christoffel; Sugiyama, Masashi: Semi-supervised learning of class balance under class-prior change by distribution matching (2014)
  20. Hutter, Frank; Xu, Lin; Hoos, Holger H.; Leyton-Brown, Kevin: Algorithm runtime prediction: methods & evaluation (2014)

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