StOpt: STochastic OPTimization library in C++. The STochastic OPTimization library (StOpt) aims at providing tools in C++ for solving some stochastic optimization problems encountered in finance or in the industry. A python binding is available for some C++ objects provided permitting to easily solve an optimization problem by regression. Different methods are available : dynamic programming methods based on Monte Carlo with regressions (global, local and sparse regressors), for underlying states following some uncontrolled Stochastic Differential Equations (python binding provided). Semi-Lagrangian methods for Hamilton Jacobi Bellman general equations for underlying states following some controlled Stochastic Differential Equations (C++ only). Stochastic Dual Dynamic Programming methods to deal with stochastic stocks management problems in high dimension. A SDDP module in python is provided. To use this module, the transitional optimization problem has to written in C++ and mapped to python (examples provided). Some methods are provided to solve by Monte Carlo some problems where the underlying stochastic state is controlled. Some pure Monte Carlo Methods are proposed to solve some non linear PDEs. For each method, a framework is provided to optimize the problem and then simulate it out of the sample using the optimal commands previously calculated. Parallelization methods based on OpenMP and MPI are provided in this framework permitting to solve high dimensional problems on clusters. The library should be flexible enough to be used at different levels depending on the user’s willingness.

References in zbMATH (referenced in 10 articles )

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  1. Dowson, Oscar; Kapelevich, Lea: SDDP.jl: a Julia package for stochastic dual dynamic programming (2021)
  2. Langrené, Nicolas; Warin, Xavier: Fast multivariate empirical cumulative distribution function with connection to kernel density estimation (2021)
  3. Gobet, Emmanuel; Pimentel, Isaque; Warin, Xavier: Option valuation and hedging using an asymmetric risk function: asymptotic optimality through fully nonlinear partial differential equations (2020)
  4. Mike Ludkovski: mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms (2020) arXiv
  5. van Ackooij, W.; Warin, X.: On conditional cuts for stochastic dual dynamic programming (2020)
  6. Alasseur, Clemence; Balata, Alessandro; Aziza, Sahar Ben; Maheshwari, Aditya; Tankov, Peter; Warin, Xavier: Regression Monte Carlo for microgrid management (2019)
  7. Bénézet, Cyril; Bonnefoy, Jérémie; Chassagneux, Jean-François; Deng, Shuoqing; Garcia Trillos, Camilo; Lenôtre, Lionel: A sparse grid approach to balance sheet risk measurement (2019)
  8. Bouchard, Bruno; Tan, Xiaolu; Warin, Xavier: Numerical approximation of general Lipschitz BSDEs with branching processes (2019)
  9. Ding L, Ahmed S, Shapiro A: A Python package for multi-stage stochastic programming (2019) not zbMATH
  10. Bouchard, Bruno; Tan, Xiaolu; Warin, Xavier; Zou, Yiyi: Numerical approximation of BSDEs using local polynomial drivers and branching processes (2017)