On Monte-Carlo methods in convex stochastic optimization
From MaRDI portal
Publication:2083277
DOI10.1214/22-AAP1781zbMath1502.90114arXiv2101.07794OpenAlexW3135775113MaRDI QIDQ2083277
Shahar Mendelson, Daniel Bartl
Publication date: 10 October 2022
Published in: The Annals of Applied Probability (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2101.07794
stochastic optimizationstochastic counterpart methodsample-path optimizationfinite sample/nonasymptotic concentration inequality
General nonlinear regression (62J02) Convex programming (90C25) Stochastic programming (90C15) Management decision making, including multiple objectives (90B50)
Related Items
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Monte Carlo methods for mean-risk optimization and portfolio selection
- Covariance estimation for distributions with \({2+\varepsilon}\) moments
- Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures
- Validation analysis of mirror descent stochastic approximation method
- Stochastic programs and statistical data
- Random vectors in the isotropic position
- Sharper bounds for Gaussian and empirical processes
- Sparse Markowitz portfolio selection by using stochastic linear complementarity approach
- Sub-Gaussian estimators of the mean of a random vector
- Regularization and the small-ball method. I: Sparse recovery
- Robust sample average approximation
- On rates of convergence for sample average approximations in the almost sure sense and in mean
- Mean estimation with sub-Gaussian rates in polynomial time
- Functional inequalities for forward and backward diffusions
- Risk minimization by median-of-means tournaments
- Mean estimation and regression under heavy-tailed distributions: A survey
- Near-optimal mean estimators with respect to general norms
- On aggregation for heavy-tailed classes
- Sharp lower bounds on the least singular value of a random matrix without the fourth moment condition
- The Sample Average Approximation Method for Stochastic Discrete Optimization
- Learning without Concentration
- Concentration Inequalities
- The Expected Norm of a Sum of Independent Random Matrices: An Elementary Approach
- Stochastic Finance
- Universal Confidence Sets for Solutions of Optimization Problems
- Bounding the Smallest Singular Value of a Random Matrix Without Concentration
- Quantitative estimates of the convergence of the empirical covariance matrix in log-concave ensembles
- Non-asymptotic confidence bounds for the optimal value of a stochastic program
- Sample Covariance Matrices of Heavy-Tailed Distributions
- A Central Limit Theorem and Hypotheses Testing for Risk-averse Stochastic Programs
- On the Geometry of Random Polytopes
- Extending the scope of the small-ball method
- An Unrestricted Learning Procedure
- Upper and Lower Bounds for Stochastic Processes
- Distribution-Invariant Risk Measures, Entropy, and Large Deviations