scientific article; zbMATH DE number 7255174
From MaRDI portal
Publication:4969260
Selvaprabu Nadarajah, Negar Soheili, Qihang Lin, Tianbao Yang
Publication date: 5 October 2020
Full work available at URL: https://arxiv.org/abs/1908.03077
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
min-max optimizationlevel set methodsconstrained stochastic optimizationstochastic gradient methodsonline validation
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Solution approaches for the multiobjective stochastic programming
- An optimal method for stochastic composite optimization
- Survey of multi-objective optimization methods for engineering
- Minimizing finite sums with the stochastic average gradient
- Pegasos: primal estimated sub-gradient solver for SVM
- Nonparametric least squares estimation of a multivariate convex regression function
- Validation analysis of mirror descent stochastic approximation method
- Multi-objective stochastic programming for portfolio selection
- Handling CVaR objectives and constraints in two-stage stochastic models
- Generalized polynomial approximations in Markovian decision processes
- Introductory lectures on convex optimization. A basic course.
- Level-set methods for convex optimization
- An optimal randomized incremental gradient method
- New variants of bundle methods
- A Smooth Perceptron Algorithm
- A smoothing stochastic gradient method for composite optimization
- On Convergence Rates of Convex Regression in Multiple Dimensions
- Distributionally Robust Convex Optimization
- From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
- Relaxations of Weakly Coupled Stochastic Dynamic Programs
- The Linear Programming Approach to Approximate Dynamic Programming
- Robust Stochastic Approximation Approach to Stochastic Programming
- A Level-Set Method for Convex Optimization with a Feasible Solution Path
- Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization I: A Generic Algorithmic Framework
- A Proximal Stochastic Gradient Method with Progressive Variance Reduction
- Testing Against a Linear Regression Model Using Ideas from Shape-Restricted Estimation
- Neyman-Pearson classification, convexity and stochastic constraints
- Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization, II: Shrinking Procedures and Optimal Algorithms
- On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
- A Stochastic Approximation Method
- On the learnability and design of output codes for multiclass problems
This page was built for publication:
