Convex optimization algorithms by dimitri p. bertsekas

  • Nonlinear programming books

    Several machine learning applications, such as neural networks, support vector machines, logistic regression, and linear regression, use convex optimization.
    The optimization problem, which is a convex optimization problem, can be effectively handled by gradient descent..

Is Adam a good algorithm for convex optimization?

We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework

Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods


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