Weakly Time-Coupled Approximation of Markov Decision Processes
Finite-horizon Markov decision processes (MDPs) with high-dimensional exogenous uncertainty and endogenous states arise in operations and finance, including the valuation and exercise of Bermudan and real options, but face a scalability barrier as computational complexity grows with the horizon. A common approximation represents the value function using basis functions, but methods for fitting weights treat cross-stage optimization differently. Least squares Monte Carlo (LSM) fits weights via backward recursion and regression, avoiding joint optimization but accumulating error over the horizon. Approximate linear programming (ALP) and pathwise optimization (PO) jointly fit weights to prod...
| Authors | Negar Soheili, Selvaprabu Nadarajah, Bo Yang |
|---|---|
| Primary category | math.OC |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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