• Adam

  • Referenced in 400 articles [sw22205]
  • Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based...
  • DOT

  • Referenced in 62 articles [sw14223]
  • Tools. DOT is a general-purpose gradient-based optimization software library that can be used...
  • Autograd

  • Referenced in 20 articles [sw22077]
  • main intended application of Autograd is gradient-based optimization. For more information, check...
  • AdaGrad

  • Referenced in 119 articles [sw22202]
  • subgradient methods for online learning and stochastic optimization. We present a new family of subgradient ... earlier iterations to perform more informative gradient-based learning. Metaphorically, the adaptation allows ... paradigm stems from recent advances in stochastic optimization and online learning which employ proximal functions...
  • SGDR

  • Referenced in 10 articles [sw30752]
  • restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence...
  • SHARK

  • Referenced in 15 articles [sw13542]
  • single- and multi-objective optimization (e.g., evolutionary and gradient-based algorithms) as well as kernel...
  • GROW

  • Referenced in 3 articles [sw18662]
  • GROW: a gradient-based optimization workflow for the automated development of molecular. The concept, issues ... implementation and file formats of the GRadient-based Optimization Workflow for the Automated Development ... atomistic molecular simulations by an iterative, gradient-based optimization workflow. The modularly constructed tool consists...
  • optimParallel

  • Referenced in 3 articles [sw24224]
  • package optimParallel: Parallel Versions of the Gradient-Based optim() Methods. Provides parallel versions ... gradient-based optim() methods. The main function of the package is optimParallel(), which...
  • LDGB

  • Referenced in 37 articles [sw07134]
  • large-scale nonsmooth optimization Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable ... problems the direct application of smooth gradient-based methods may lead to a failure ... hand, none of the current general nonsmooth optimization methods is efficient in large-scale settings...
  • GRANSO

  • Referenced in 17 articles [sw38500]
  • GRANSO: GRadient-based Algorithm for Non-Smooth Optimization. GRANSO is an optimization package implemented...
  • scikit-optimize

  • Referenced in 2 articles [sw37048]
  • implements several methods for sequential model-based optimization. skopt aims to be accessible and easy ... Scikit-Learn. We do not perform gradient-based optimization. For gradient-based optimization algorithms look...
  • Krotov

  • Referenced in 3 articles [sw32294]
  • Krotov’s method compares to other gradient-based optimization methods such as gradient-ascent...
  • AESOP

  • Referenced in 3 articles [sw02849]
  • aerodynamic shape optimization. Aerodynamic shape optimization based on Computational Fluid Dynamics can automatically improve ... shape parameterization and algorithms for gradient-based optimization. The result is a portable and efficient...
  • DOSI

  • Referenced in 2 articles [sw27054]
  • with local credit assignment. A novel swarm-based algorithm is proposed for the training ... search algorithm to find optimal weight values. While gradient-based methods, such as backpropagation ... yield a globally optimal solution. To overcome the limitations of gradient-based methods, evolutionary algorithms...
  • Algorithm 1008

  • Referenced in 1 article [sw35943]
  • step method for, among other applications, gradient-based optimization and optimum control problems. The algebra...
  • Evolino

  • Referenced in 18 articles [sw36450]
  • Evolino: hybrid neuroevolution / optimal linear search for sequence learning. Current Neural Network learning algorithms ... linear dynamical systems. Most supervised gradient-based recurrent neural networks (RNNs) suffer from a vanishing ... linear regression or quadratic programming to compute optimal linear mappings from hidden state to output...
  • Far-HO

  • Referenced in 1 article [sw25680]
  • Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning. In (Franceschi ... bilevel programming, that encompasses gradient-based hyperparameter optimization and meta-learning. We formulated an approximate...
  • PyFLOSIC

  • Referenced in 1 article [sw33445]
  • self-interaction correction (FLO-SIC), which is based on the Python simulation of chemistry frame ... within the local density approximation (LDA), generalized-gradient approximation (GGA), and meta-GGA provided ... initialized automatically within PyFLOSIC and optimized with an interface to the atomic simulation environment ... variety of powerful gradient-based algorithms for geometry optimization. Although PyFLOSIC has already facilitated applications...
  • pyOptSparse

  • Referenced in 2 articles [sw37949]
  • used in pyOptSparse, including both gradient-based and gradient-free methods. A visualization tool called ... comes packaged with pyOptSparse, which shows the optimization history through an interactive...
  • R-SPLINE

  • Referenced in 12 articles [sw29802]
  • interpolation and neighborhood enumeration. We consider simulation-optimization (SO) models where the decision variables ... among the first few gradient-based search algorithms tailored for solving integer-ordered local...