A modified MSA for stochastic control problems

Bekzhan Kerimkulov, David Siska, Lukasz Szpruch

Research output: Contribution to journalArticlepeer-review


The classical Method of Successive Approximations (MSA) is an iterative method for solving stochastic control problems and is derived from Pontryagin's optimality principle. It is known that the MSA may fail to converge. Using careful estimates for the backward stochastic differential equation (BSDE) this paper suggests a modification to the MSA algorithm. This modified MSA is shown to converge for general stochastic control problems with control in both the drift and diffusion coefficients. Under some additional assumptions the rate of convergence is shown. The results are valid without restrictions on the time horizon of the control problem, in contrast to iterative methods based on the theory of forward-backward stochastic differential equations.
Original languageEnglish
Number of pages15
JournalApplied Mathematics and Optimization
Publication statusPublished - 25 Feb 2021


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