Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
We consider a Markov Decision Process problem with risk related constraint. The constraint is a linearized variance approximation. We find a policy that maximizes a ratio of the reward expectation to its linearized variance. We show that under monotonicity assumption which is natural for risk related problem the Simplex algorithm with Gass-Saaty shadow-vertex pivoting rule is strongly polynomial for both cost models: discounted and expected average for infinite horizon. We show an application of the algorithm to the problem of maximization of the Sharpe ratio.
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Natalia Martinez Gil, Dhaval Patel, et al.
UAI 2024
Shubhi Asthana, Pawan Chowdhary, et al.
KDD 2021
Pavithra Harsha, Ali Koc, et al.
INFORMS 2021