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.
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References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Schmidt, Mark; Le Roux, Nicolas; Bach, Francis: Minimizing finite sums with the stochastic average gradient (2017)
- 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)
- Bogosel, Beniamin; Oudet, E\'douard: Qualitative and numerical analysis of a spectral problem with perimeter constraint (2016)
- Hirayama, Jun-ichiro; Hyvärinen, Aapo; Ishii, Shin: Sparse and low-rank matrix regularization for learning time-varying Markov networks (2016)
- Lazar, Markus; Jarre, Florian: Calibration by optimization without using derivatives (2016)
- Vaksman, Gregory; Zibulevsky, Michael; Elad, Michael: Patch ordering as a regularization for inverse problems in image processing (2016)
- du Plessis, Marthinus Christoffel; Sugiyama, Masashi: Semi-supervised learning of class balance under class-prior change by distribution matching (2014)
- Hutter, Frank; Xu, Lin; Hoos, Holger H.; Leyton-Brown, Kevin: Algorithm runtime prediction: methods & evaluation (2014)
- 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)