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1. Stochastic Constraints: How feasible is feasible?

   Eckman, David J; Henderson, Shane G; Shashaani, S (December 2023, ACM)

   Corlu, C G; Hunter, S R; Lam, H; Onggo, B S; Shortle, J; Biller, B (Ed.)

   Stochastic constraints, which constrain an expectation in the context of simulation optimization, can be hard to conceptualize and harder still to assess. As with a deterministic constraint, a solution is considered either feasible or infeasible with respect to a stochastic constraint. This perspective belies the subjective nature of stochastic constraints, which often arise when attempting to avoid alternative optimization formulations with multiple objectives or an aggregate objective with weights. Moreover, a solution’s feasibility with respect to a stochastic constraint cannot, in general, be ascertained based on only a finite number of simulation replications. We introduce different means of estimating how “close” the expected performance of a given solution is to being feasible with respect to one or more stochastic constraints. We explore how these metrics and their bootstrapped error estimates can be incorporated into plots showing a solver’s progress over time when solving a stochastically constrained problem. 

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2. Stochastic Constraints: How Feasible Is Feasible?

   https://doi.org/10.1109/WSC60868.2023.10408734  

   Eckman, David J; Henderson, Shane G; Shashaani, Sara (December 2023, Proceedings of the 2023 Winter Simulation Conference, IEEE)

   Stochastic constraints, which constrain an expectation in the context of simulation optimization, can be hard to conceptualize and harder still to assess. As with a deterministic constraint, a solution is considered either feasible or infeasible with respect to a stochastic constraint. This perspective belies the subjective nature of stochastic constraints, which often arise when attempting to avoid alternative optimization formulations with multiple objectives or an aggregate objective with weights. Moreover, a solution’s feasibility with respect to a stochastic constraint cannot, in general, be ascertained based on only a finite number of simulation replications. We introduce different means of estimating how “close” the expected performance of a given solution is to being feasible with respect to one or more stochastic constraints. We explore how these metrics and their bootstrapped error estimates can be incorporated into plots showing a solver’s progress over time when solving a stochastically constrained problem. 

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3. Is isochoric vitrification feasible?

   https://doi.org/10.1016/j.cryobiol.2023.03.007  

   Solanki, Prem K.; Rabin, Yoed (June 2023, Cryobiology)
4. Top Feasible Arm Identification

   Katz-Samuels, Julian; Scott, Clayton (January 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS))

   We propose a new variant of the top arm identification problem, top feasible arm identification, where there are K arms associated with D-dimensional distributions and the goal is to find m arms that maximize some known linear function of their means subject to the constraint that their means belong to a given set P € R D. This problem has many applications since in many settings, feedback is multi-dimensional and it is of interest to perform constrained maximization. We present problem-dependent lower bounds for top feasible arm identification and upper bounds for several algorithms. Our most broadly applicable algorithm, TF-LUCB-B, has an upper bound that is loose by a factor of OpDlogpKqq. Many problems of practical interest are two dimensional and, for these, it is loose by a factor of OplogpKqq. Finally, we conduct experiments on synthetic and real-world datasets that demonstrate the effectiveness of our algorithms. Our algorithms are superior both in theory and in practice to a naive two-stage algorithm that first identifies the feasible arms and then applies a best arm identification algorithm to the feasible arms. 

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5. Feasible Joint Posterior Beliefs

   https://doi.org/10.1145/3391403.3399505  

   Arieli, I.; Babichenko, Y.; Sandomirskiy, F.; Tamuz, O (September 2021, The journal of political economy)