A Stochastic Approximation Method for Simulation-Based Quantile Optimization
From MaRDI portal
Publication:5060775
DOI10.1287/ijoc.2022.1214OpenAlexW4286471186WikidataQ114058179 ScholiaQ114058179MaRDI QIDQ5060775
Qi Zhang, Yijie Peng, Jiaqiao Hu, Gongbo Zhang
Publication date: 11 January 2023
Published in: INFORMS Journal on Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1287/ijoc.2022.1214
Cites Work
- Unnamed Item
- On reducing a quantile optimization problem with discrete distribution to a mixed integer programming problem
- A method for solving quantile optimization problems with a bilinear loss function
- Stochastic approximation. A dynamical systems viewpoint.
- Guaranteeing approach to solving quantile optimization problems
- Stochastic approximation with two time scales
- Applications of generalized likelihood ratio method to distribution sensitivities and steady-state simulation
- Optimization of the quantile criterion for the convex loss function by a stochastic quasigradient algorithm
- A Measure-Valued Differentiation Approach to Sensitivities of Quantiles
- Conditional Monte Carlo Estimation of Quantile Sensitivities
- Technical Note—On Estimating Quantile Sensitivities via Infinitesimal Perturbation Analysis
- Adaptive multivariate three-timescale stochastic approximation algorithms for simulation based optimization
- A New Unbiased Stochastic Derivative Estimator for Discontinuous Sample Performances with Structural Parameters
- A Multilevel Simulation Optimization Approach for Quantile Functions
- Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling
- Kernel estimation of quantile sensitivities
- Estimating Quantile Sensitivities
- Simulation Optimization Using Multi-Time-Scale Adaptive Random Search
This page was built for publication: A Stochastic Approximation Method for Simulation-Based Quantile Optimization
