minFunc

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 16 articles )

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  1. An, Congpei; Xiao, Yuchen: Numerical construction of spherical (t)-designs by Barzilai-Borwein method (2020)
  2. Ahmed, Ali; Aghasi, Alireza; Hand, Paul: Simultaneous phase retrieval and blind deconvolution via convex programming (2019)
  3. Driggs, Derek; Becker, Stephen; Aravkin, Aleksandr: Adapting regularized low-rank models for parallel architectures (2019)
  4. Gao, Wenbo; Goldfarb, Donald: Block BFGS methods (2018)
  5. Brockmeier, Austin J.; Mu, Tingting; Ananiadou, Sophia; Goulermas, John Y.: Quantifying the informativeness of similarity measurements (2017)
  6. Hamdi, Hamidreza; Couckuyt, Ivo; Sousa, Mario Costa; Dhaene, Tom: Gaussian processes for history-matching: application to an unconventional gas reservoir (2017)
  7. Schmidt, Mark; Le Roux, Nicolas; Bach, Francis: Minimizing finite sums with the stochastic average gradient (2017)
  8. 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)
  9. Bogosel, Beniamin; Oudet, Édouard: Qualitative and numerical analysis of a spectral problem with perimeter constraint (2016)
  10. Hirayama, Jun-ichiro; Hyvärinen, Aapo; Ishii, Shin: Sparse and low-rank matrix regularization for learning time-varying Markov networks (2016)
  11. Lazar, Markus; Jarre, Florian: Calibration by optimization without using derivatives (2016)
  12. Vaksman, Gregory; Zibulevsky, Michael; Elad, Michael: Patch ordering as a regularization for inverse problems in image processing (2016)
  13. Brockmeier, Austin J.; Choi, John S.; Kriminger, Evan G.; Francis, Joseph T.; Principe, Jose C.: Neural decoding with kernel-based metric learning (2014)
  14. du Plessis, Marthinus Christoffel; Sugiyama, Masashi: Semi-supervised learning of class balance under class-prior change by distribution matching (2014)
  15. Hutter, Frank; Xu, Lin; Hoos, Holger H.; Leyton-Brown, Kevin: Algorithm runtime prediction: methods & evaluation (2014)
  16. Pfaller, S.; Possart, G.; Steinmann, P.; Rahimi, M.; Müller-Plathe, F.; Böhm, M. C.: A comparison of staggered solution schemes for coupled particle-continuum systems modeled with the Arlequin method (2012)