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Explores the convexity of Lovász extension and submodular function maximization, focusing on extending functions to convex sets and proving their convexity.
Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
Explores convex optimization, emphasizing the importance of minimizing functions within a convex set and the significance of continuous processes in studying convergence rates.
