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Dealing with singularities in nonlinear unconstrained optimization

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Dealing with singularities in nonlinear unconstrained optimization

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Scalable Convex Optimization Methods for Semidefinite Programming

Distributed Optimization and Control using Operator Splitting Methods

Deep Learning for Logic Optimization Algorithms, 2018 IEEE International Symposium on Circuits and Systems (ISCAS)

Deep Learning for Logic Optimization Algorithms

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

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Splitting Methods for Distributed Optimization and Control

Memetic Viability Evolution for Constrained Optimization

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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Scalable Convex Optimization Methods for Semidefinite Programming

Deep Learning for Logic Optimization Algorithms, 2018 IEEE International Symposium on Circuits and Systems (ISCAS)

Distributed Optimization and Control using Operator Splitting Methods

Deep Learning for Logic Optimization Algorithms

Splitting Methods for Distributed Optimization and Control

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

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

Memetic Viability Evolution for Constrained Optimization