Robust Optimization in Radiation Therapy

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Covers the concept of linear optimization and the duality relationship between primal and dual problems.

Covers semi-definite programming and optimization over positive semidefinite cones.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

Discusses proximal and subgradient descent methods for optimization in machine learning.

Explores implicit bias, gradient descent, stability in optimization algorithms, and generalization bounds in machine learning.

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.