Methods and systems for automatic differentiation of discrete and discrete-continuous stochastic programs
AssigneesJuliaHub, Inc., University of Basel
InventorsChristopher Rackauckas, Moritz Schauer, Frank Schäfer, Gaurav Arya
Life of this patent
Estimating the term…
Abstract
Systems and methods for computation of derivatives of stochastic programs are disclosed. A system having at least one processor is configured for automatically determining a derivative of an expectation of a stochastic program with respect to its input parameters, where the stochastic program includes continuous and discrete sources of randomness.
Claims20
A programmatic method for performing language-level automatic differentiation of a stochastic program, the programmatic method comprising:
- receiving a user-provided stochastic program;
- generating a stochastic derivative based on the user-provided stochastic program;
- and
- generating an estimator for the user-provided stochastic program using the stochastic derivative,
- wherein the estimator determines an estimate of a derivative of the user-provided stochastic program.
A computer system for computation of derivatives of stochastic programs, the computer system comprising:
- a memory and at least one processor, the at least one processor configured for:
- automatically determining a derivative of an expectation of a user-provided stochastic program based on input parameters of the user-provided stochastic program, wherein the user-provided stochastic program includes continuous and discrete sources of randomness.
A programmatic method for computation of derivatives of stochastic programs, the programmatic method comprising:
- automatically determining a derivative of an expectation of a user-provided stochastic program with respect to input parameters of the user-provided stochastic program, wherein the user-provided stochastic program includes continuous and discrete sources of randomness.
Dependencies are inferred from the claim text.