The algorithm is based on a projected gradient iteration applied to the constraint fulfillment problem. The computer time required by the algorithm also scales approximately linearly with the number ...

On heterogeneous cluster systems, the convergence performances of neural network models are greatly troubled by the different performances of machines. In this paper, we propose a novel distributed ...

In noise-free systems, the recently proposed distributed linear regression algorithm, named the Iteratively Pre-conditioned Gradient-descent (IPG) method, has been claimed to converge faster than ...

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AlphaGrad: Normalized Gradient Descent for Adaptive Multi-loss Functions in EEG-based Motor Imagery Classification. multi-task-learning motor-imagery brain-computer-interfaces adaptive-loss-blending ...

Spin-projected unrestricted Hartree–Fock (SUHF) theory is a valuable method that effectively addresses static correlation. To further enhance its accuracy, it is important to augment it with dynamic ...

Jan 28: Function optimization using first and second order gradient methods Goal: Review gradient descent approaches. A nice chapter on function optimization techniques: Numerical Recipes in C , ...