• Adam

  • Referenced in 567 articles [sw22205]
  • Adam: A Method for Stochastic Optimization. We introduce Adam, an algorithm for first-order gradient ... based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments ... best known results under the online convex optimization framework. Empirical results demonstrate that Adam works ... practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant...
  • LINDO

  • Referenced in 548 articles [sw04448]
  • Programming, Linear Programming, Nonlinear Programming, Stochastic Programming, Global Optimization LINDO Application Programming Interface (LINDO...
  • AdaGrad

  • Referenced in 141 articles [sw22202]
  • subgradient methods for online learning and stochastic optimization. We present a new family of subgradient ... paradigm stems from recent advances in stochastic optimization and online learning which employ proximal functions...
  • LINGO

  • Referenced in 310 articles [sw04942]
  • Quadratically Constrained, Second Order Cone, Stochastic, and Integer optimization models faster, easier and more efficient...
  • CMA-ES

  • Referenced in 114 articles [sw05063]
  • Evolution strategies (ES) are stochastic, derivative-free methods for numerical optimization of non-linear ... convex continuous optimization problems. They belong to the class of evolutionary algorithms and evolutionary computation ... generated by variation, usually in a stochastic way, and then some individuals are selected ... Quasi-Newton method in classical optimization. In contrast to most classical methods, fewer assumptions...
  • Duali

  • Referenced in 26 articles [sw01245]
  • designed to solve deterministic and stochastic optimal control models of economic systems. The Duali part ... useful for teaching about dynamic deterministic and stochastic economic models. It is also a useful...
  • Pegasos

  • Referenced in 100 articles [sw08752]
  • simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast ... training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require...
  • EOlib

  • Referenced in 19 articles [sw00239]
  • helps you to write your own stochastic optimization algorithms insanely fast.Evolutionary algorithms forms a family ... produce the best results. These are stochastic algorithms, because they iteratively use random processes ... these methods are used to solve optimization problems, and may be also called ”metaheuristics”. They...
  • S-TaLiRo

  • Referenced in 20 articles [sw09775]
  • randomized testing based on stochastic optimization techniques including Monte-Carlo methods and Ant-Colony Optimization...
  • MLMSRBF

  • Referenced in 41 articles [sw07571]
  • stochastic radial basis function method for the global optimization of expensive functions We introduce ... global optimization of computationally expensive multimodal functions when derivatives are unavailable. The proposed Stochastic Response ... previously evaluated points. We develop a global optimization version and a multistart local optimization version...
  • StOpt

  • Referenced in 9 articles [sw32903]
  • StOpt: STochastic OPTimization library in C++. The STochastic OPTimization library (StOpt) aims at providing tools ... solving some stochastic optimization problems encountered in finance or in the industry. A python binding ... objects provided permitting to easily solve an optimization problem by regression. Different methods are available ... regressors), for underlying states following some uncontrolled Stochastic Differential Equations (python binding provided). Semi-Lagrangian...
  • t-SNE

  • Referenced in 113 articles [sw22300]
  • Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces...
  • ASTRO-DF

  • Referenced in 11 articles [sw26833]
  • trust-region algorithms for derivative-free stochastic optimization. We consider unconstrained optimization problems where only ... region algorithms, where a stochastic local model is constructed, optimized, and updated iteratively. Function estimation ... first-order critical points when using stochastic polynomial interpolation models. The question of using more...
  • DAKOTA

  • Referenced in 74 articles [sw05202]
  • optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion ... within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under...
  • MCELL

  • Referenced in 23 articles [sw06121]
  • useful and stochastic behavior dominates. MCell uses highly optimized Monte Carlo algorithms to track...
  • SDDP

  • Referenced in 6 articles [sw27099]
  • package for solving large multistage convex stochastic optimization problems using stochastic dual dynamic programming ... reasonable amount of background knowledge about stochastic optimization, the SDDP algorithm, Julia, and JuMP...
  • Pyomo

  • Referenced in 53 articles [sw04910]
  • create Pyomo models, apply a variety of optimizers, and examine solutions. The text begins with ... cover advanced topics such as nonlinear models, stochastic models, and scripting examples...
  • SGDLibrary

  • Referenced in 5 articles [sw26680]
  • SGDLibrary: a MATLAB library for stochastic optimization algorithms. We consider the problem of finding ... scale data is to use a stochastic optimization algorithm to solve the problem. SGDLibrary ... MATLAB library of a collection of stochastic optimization algorithms. The purpose of the library...
  • SMS

  • Referenced in 29 articles [sw01085]
  • automatic differentiation technique, simultaneous optimization of expressions and a stochastic evaluation of the formulas instead...
  • OPTCON

  • Referenced in 14 articles [sw02660]
  • OPTCON: An algorithm for the optimal control of nonlinear stochastic models. The authors describe ... algorithm for the optimal control of nonlinear dynamic control that allows for additive uncertainty ... well as for the presence of a stochastic parameter vector in the system equations ... Bellman’s principle of optimality to solve the problem. These two steps are repeated until...