Computational Aspects of Optimization

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Neural Networks: Training and Optimization

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Optimization Techniques: Local Search, VNS, Simulated Annealing

Explores optimization techniques like local search, VNS, and simulated annealing.

Optimization Methods

Covers unconstrained and constrained optimization, optimal control, neural networks, and global optimization methods.

Feed-forward Networks

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

High Performance OPF Solvers

Explores high-performance OPF solvers, addressing challenges in power system optimization and showcasing significant speed-ups and memory-efficient approaches.

Energy Systems Optimization

Explores energy systems modeling, optimization, and cost analysis for efficient operations.

Optimization Principles

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

Stochastic Gradient Descent

Explores stochastic gradient descent optimization and the Mean-Field Method in neural networks.

Applications of GAMP

Delves into applying the GAMP algorithm to simplify the lasso problem and analyze optimization challenges in neural networks.