A sensitivity analysis reduces nonlinear Kolmogorov PDEs (nonlinearity from ε-neighborhood max over drifts/diffusions) to a linear PDE plus ε times a second linear PDE, enabling efficient high-dimensional Monte Carlo approximation with error bounds.
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Numerical method for nonlinear Kolmogorov PDEs via sensitivity analysis
A sensitivity analysis reduces nonlinear Kolmogorov PDEs (nonlinearity from ε-neighborhood max over drifts/diffusions) to a linear PDE plus ε times a second linear PDE, enabling efficient high-dimensional Monte Carlo approximation with error bounds.