Abstract: Unmeasured confounding is a major source of skepticism about the validity of findings in observational studies. To address this issue, negative control methods and proximal causal inference ...

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This study introduced an efficient method for solving non-linear equations. Our approach enhances the traditional spectral conjugate gradient parameter, resulting in significant improvements in the ...

Language-based agentic systems represent a breakthrough in artificial intelligence, allowing for the automation of tasks such as question-answering, programming, and advanced problem-solving. These ...

Adam is widely used in deep learning as an adaptive optimization algorithm, but it struggles with convergence unless the hyperparameter β2 is adjusted based on the specific problem. Attempts to fix ...

Provides proximal operator evaluation routines and proximal optimization algorithms, such as (accelerated) proximal gradient methods and alternating direction method of multipliers (ADMM), for ...

PyTorch implementation of Proximal Gradient Algorithms a la Parikh and Boyd (2014). Useful for Auto-Sizing (Murray and Chiang 2015, Murray et al. 2019).

ABSTRACT: Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of ...