Mathematical procedures for minimising or maximising objective functions, central to training machine learning models. Gradient-based methods (SGD, Adam, RMSprop, AdaGrad) iteratively update model parameters to reduce loss; advanced techniques encompass momentum-based optimisation, adaptive learning rates, second-order methods (L-BFGS, natural gradient), and gradient-free approaches (evolutionary strategies, Bayesian optimisation), addressing non-convexity, saddle points, and high-dimensional parameter-space challenges.
Semantic Classification
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Key Characteristics
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Employs gradient descent and its variants
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Adapts learning rates dynamically during training
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Incorporates momentum and acceleration techniques
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Handles large-scale distributed optimization
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Balances convergence speed and stability
Overview
Optimization Algorithms in AI are mathematical procedures for minimizing or maximizing objective functions, central to training machine learning models. Gradient-based methods (SGD, Adam, RMSprop, AdaGrad) iteratively update model parameters to minimize loss functions. Advanced techniques include momentum-based optimization, adaptive learning rates, second-order methods (L-BFGS, natural gradient), and gradient-free optimization (evolutionary strategies, Bayesian optimization). Optimization algorithms address challenges of non-convexity, saddle points, vanishing/exploding gradients, and computational efficiency in high-dimensional parameter spaces.
Related Concepts
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References
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Kingma, D. & Ba, J. (2015). Adam: A Method for Stochastic Optimization. ICLR 2015.
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Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv:1609.04747.
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Duchi, J. et al. (2011). Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. JMLR 12, 2121-2159.