Convex Optimization: Gradient Algorithms

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Covers optimization without constraints using the gradient method to find the function's minimum.

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

Covers stochastic optimization, sparsity, and non-smooth minimization via subgradient descent.

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Explores the role of computation in data mathematics, focusing on iterative methods, optimization, estimators, and descent principles.

Explores optimization techniques like gradient descent, line search, and Newton's method for efficient problem-solving.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Covers the optimality of convergence rates in accelerated and stochastic gradient descent methods for non-convex optimization problems.