- Referenced in 218 articles
- 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 ... require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed ... analyze the theoretical convergence properties of the algorithm and provide a regret bound...
- Referenced in 109 articles
- implementation of a nested decomposition algorithm for the multistage stochastic linear programming problem. Many ... deterministic staircase problems are adapted to the stochastic setting and their effect on computation times ... Numerical results compare the performance of the algorithm to MINOS...
- Referenced in 70 articles
- code software implementing a an efficient stochastic search algorithm for for exploring spaces of Gaussian...
- Referenced in 56 articles
- behavior using ODEs or Gillespie’s stochastic simulation algorithm; arbitrary discrete events can be included...
- Referenced in 85 articles
- analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem ... training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require ... size of the training set, the resulting algorithm is especially suited for learning from large...
- Referenced in 87 articles
- ADAGRAD: adaptive gradient algorithm; Adaptive subgradient methods for online learning and stochastic optimization. We present ... paradigm stems from recent advances in stochastic optimization and online learning which employ proximal functions ... control the gradient steps of the algorithm. We describe and analyze an apparatus for adaptively...
- Referenced in 19 articles
- helps you to write your own stochastic optimization algorithms insanely fast.Evolutionary algorithms forms a family ... algorithms inspired by the theory of evolution, that solve various problems. They evolve ... produce the best results. These are stochastic algorithms, because they iteratively use random processes...
- Referenced in 32 articles
- Singh’s E^3 algorithm, covering zero-sum stochastic games. (2) It has a built ... under uncertainty” bias used in many RL algorithms. (4) It is simpler, more general ... Tennenholtz’s LSG algorithm for learning in single controller stochastic games. (5) It generalizes...
- Referenced in 22 articles
- gradient descent. The SGD-QN algorithm is a stochastic gradient descent algorithm that makes careful ... stochastic gradient descent but requires less iterations to achieve the same accuracy. This algorithm...
- Referenced in 25 articles
- evidential reasoning in large Bayesian networks Stochastic sampling algorithms, while an attractive alternative to exact...
- Referenced in 33 articles
- Logical and stochastic modeling with smart. We describe the main features of Smart, a software ... model-checking algorithms, are available. For the study of stochastic and timing behavior, both sparse ... simulation is always applicable regardless of the stochastic nature of the process, but certain classes ... easy integration of new formalisms and solution algorithms...
- Referenced in 24 articles
- Parallel stochastic gradient algorithms for large-scale matrix completion. This paper develops Jellyfish, an algorithm...
- Referenced in 137 articles
- queue, the transient M/M/1 queue). The algorithms in this software package are based on methods ... book H.C. Tijms, A First Course in Stochastic Models, Wiley...
- Referenced in 10 articles
- package GillespieSSA: Gillespie’s Stochastic Simulation Algorithm (SSA). GillespieSSA provides a simple to use, intuitive ... extensible interface to several stochastic simulation algorithms for generating simulated trajectories of finite population continuous ... Currently it implements Gillespie’s exact stochastic simulation algorithm (Direct method) and several approximate methods...
- Referenced in 11 articles
- direct stochastic algorithm for global search This paper presents a new algorithm called probabilistic global...
- Referenced in 22 articles
- uses highly optimized Monte Carlo algorithms to track the stochastic behavior of discrete molecules...
- Referenced in 100 articles
- Adaptation Evolution Strategy. Evolution strategies (ES) are stochastic, derivative-free methods for numerical optimization ... They belong to the class of evolutionary algorithms and evolutionary computation. An evolutionary algorithm ... generated by variation, usually in a stochastic way, and then some individuals are selected...
- Referenced in 102 articles
- distribution functions”, J. P. Nolan, Commun. Statist.-Stochastic Models, 13(4), 759-774 (1997). Also ... version of Chambers, Mallows and Stuck’s algorithm to generate stable random variates. It also...
- Referenced in 42 articles
- Stochastic Gradient Descent. Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state...
- Referenced in 46 articles
- programming, mesh generation, stochastic differential equations, ﬁnancial mathematics, and veriﬁcation. The algorithms in the toolbox...