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Exact Diffusion for Distributed Optimization and Learning-Part II: Convergence Analysis

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Exact Diffusion for Distributed Optimization and Learning-Part II: Convergence Analysis

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A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Exact Diffusion for Distributed Optimization and Learning---Part II: Convergence Analysis

Global Identification of Kinetic Parameters via the Extent-based Incremental Approach

Asynchronous updates for stochastic gradient descent

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach

Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach

Real-Time Optimization Based on Adaptation of Surrogate Models

Splitting Methods for Distributed Optimization and Control

Quantization Design for Unconstrained Distributed Optimization

Real-Time Optimization Based on Adaptation of Surrogate Models

Global Identification of Kinetic Parameters via the Extent-based Incremental Approach

Asynchronous updates for stochastic gradient descent

Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Exact Diffusion for Distributed Optimization and Learning---Part II: Convergence Analysis

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach

Splitting Methods for Distributed Optimization and Control

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach

Quantization Design for Unconstrained Distributed Optimization