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#stochastic optimization

7 results
q-fin.CP2019

Pricing American Options by Exercise Rate Optimization

Christian Bayer, Raúl Tempone, Sören Wolfers

The paper proposes a Monte‑Carlo based method that optimizes randomized exercise rates to price American options, formulating a differentiable stochastic optimization problem whose…

#american options#monte carlo simulation#exercise rate optimization#option pricing
math.OC2019

Stochastic and Simulation-based Models for Setting Flow Rates in Collaborative Trajectory Options Program (CTOP)

Guodong Zhu, Peng Wei, Robert Hoffman +1

The paper studies how to set traffic flow rates in the Collaborative Trajectory Options Program (CTOP) by evaluating stochastic and simulation‑based optimization models, identifyin…

#air traffic flow management#stochastic optimization#simulation-based optimization#collaborative trajectory options
math.OC2019

Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing Approach

Zhaohao Ding, Feng Zhu, Yajing Wang +2

The paper proposes a stochastic, benefit‑sharing pricing scheme for flexible demand resources in microgrids, using chance‑constrained optimization to evaluate economic benefits and…

#demand side management#microgrid pricing#stochastic optimization#chance constraints
cs.DS2019

Algorithms to Approximate Column-Sparse Packing Problems

Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan +1

The paper introduces two techniques—non-uniform attenuation and multiple-chance algorithms—to improve approximation guarantees for column‑sparse packing problems, achieving near‑op…

#approximation algorithms#packing problems#column-sparse#stochastic optimization
math.OC2019

Inexact Newton Methods for Stochastic Nonconvex Optimization with Applications to Neural Network Training

Thomas O'Leary-Roseberry, Nick Alger, Omar Ghattas

The paper develops stochastic inexact Newton methods for nonconvex optimization and applies them to train convolutional autoencoders on MNIST and CIFAR10, showing faster convergenc…

#stochastic optimization#inexact newton methods#nonconvex optimization#neural network training
cs.LG2019

SVGD: A Virtual Gradients Descent Method for Stochastic Optimization

Zheng Li, Shi Shu

The paper introduces Stochastic Virtual Gradient Descent (SVGD), a memory‑efficient stochastic optimization algorithm that defines gradients via computational graphs and automatic…

#stochastic optimization#gradient descent#automatic differentiation#computational graph
math.OC2019

Improved Oracle Complexity of Variance Reduced Methods for Nonsmooth Convex Stochastic Composition Optimization

Tianyi Lin, Chenyou Fan, Mengdi Wang

The paper analyzes stochastic compositional variance reduced gradient methods for nonsmooth convex composition problems and proves improved incremental first-order oracle complexit…

#stochastic optimization#variance reduction#nonsmooth convex#compositional optimization