Pages that link to "Item:Q3648521"
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The following pages link to Robust Stochastic Approximation Approach to Stochastic Programming (Q3648521):
Displaying 50 items.
- Berry-Esseen bounds for multivariate nonlinear statistics with applications to M-estimators and stochastic gradient descent algorithms (Q2137030) (← links)
- Stochastic approximation method using diagonal positive-definite matrices for convex optimization with fixed point constraints (Q2138441) (← links)
- Adaptive primal-dual stochastic gradient method for expectation-constrained convex stochastic programs (Q2146450) (← links)
- Online statistical inference for parameters estimation with linear-equality constraints (Q2146461) (← links)
- Riemannian stochastic fixed point optimization algorithm (Q2159421) (← links)
- Stochastic saddle-point optimization for the Wasserstein barycenter problem (Q2162697) (← links)
- Frameworks and results in distributionally robust optimization (Q2165596) (← links)
- On finite termination of an inexact proximal point algorithm (Q2171164) (← links)
- Approximation of probabilistic constraints in stochastic programming problems with a probability measure kernel (Q2173180) (← links)
- Statistical inference for model parameters in stochastic gradient descent (Q2176618) (← links)
- Algorithms for stochastic optimization with function or expectation constraints (Q2181600) (← links)
- The subdifferential of measurable composite max integrands and smoothing approximation (Q2189440) (← links)
- Stochastic AUC optimization with general loss (Q2191849) (← links)
- Bridging the gap between constant step size stochastic gradient descent and Markov chains (Q2196224) (← links)
- Generalized gradients in dynamic optimization, optimal control, and machine learning problems (Q2215292) (← links)
- Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems (Q2220653) (← links)
- Bi-fidelity stochastic gradient descent for structural optimization under uncertainty (Q2221705) (← links)
- Why random reshuffling beats stochastic gradient descent (Q2227529) (← links)
- Forward-reflected-backward method with variance reduction (Q2231039) (← links)
- On a multistage discrete stochastic optimization problem with stochastic constraints and nested sampling (Q2235138) (← links)
- Multi-echelon supply chains with lead times and uncertain demands. A lot-sizing formulation and solutions (Q2241314) (← links)
- Bundle methods for sum-functions with ``easy'' components: applications to multicommodity network design (Q2248747) (← links)
- Incrementally updated gradient methods for constrained and regularized optimization (Q2251572) (← links)
- Convergence of online mirror descent (Q2278461) (← links)
- Optimal stochastic extragradient schemes for pseudomonotone stochastic variational inequality problems and their variants (Q2282819) (← links)
- A new convergent hybrid learning algorithm for two-stage stochastic programs (Q2286915) (← links)
- Cauchy noise loss for stochastic optimization of random matrix models via free deterministic equivalents (Q2287214) (← links)
- Algorithms of robust stochastic optimization based on mirror descent method (Q2289049) (← links)
- Stochastic subgradient method converges on tame functions (Q2291732) (← links)
- Communication-efficient algorithms for decentralized and stochastic optimization (Q2297648) (← links)
- Faster subgradient methods for functions with Hölderian growth (Q2297653) (← links)
- A stochastic trust region method for unconstrained optimization problems (Q2298821) (← links)
- Lower error bounds for the stochastic gradient descent optimization algorithm: sharp convergence rates for slowly and fast decaying learning rates (Q2303416) (← links)
- Fast and strong convergence of online learning algorithms (Q2305549) (← links)
- Stochastic sampling for deterministic structural topology optimization with many load cases: density-based and ground structure approaches (Q2310028) (← links)
- On the linear convergence of the stochastic gradient method with constant step-size (Q2311205) (← links)
- Self-concordant inclusions: a unified framework for path-following generalized Newton-type algorithms (Q2316618) (← links)
- Online estimation of the asymptotic variance for averaged stochastic gradient algorithms (Q2317311) (← links)
- Control variates for stochastic gradient MCMC (Q2329787) (← links)
- Sample average approximation with sparsity-inducing penalty for high-dimensional stochastic programming (Q2330643) (← links)
- Random minibatch subgradient algorithms for convex problems with functional constraints (Q2338088) (← links)
- On the convergence properties of non-Euclidean extragradient methods for variational inequalities with generalized monotone operators (Q2340520) (← links)
- A smooth penalty-based sample average approximation method for stochastic complementarity problems (Q2346632) (← links)
- On efficient randomized algorithms for finding the PageRank vector (Q2354453) (← links)
- On the efficiency of a randomized mirror descent algorithm in online optimization problems (Q2354481) (← links)
- Stochastic optimization on social networks with application to service pricing (Q2355196) (← links)
- On sample size control in sample average approximations for solving smooth stochastic programs (Q2376122) (← links)
- Parallel stochastic gradient algorithms for large-scale matrix completion (Q2392935) (← links)
- Random gradient-free minimization of convex functions (Q2397749) (← links)
- Robust solutions to box-constrained stochastic linear variational inequality problem (Q2410509) (← links)
