Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms

By J. Snyman. 2005

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Finding the global optimum in such equations has been the subject of decades of study (Snyman, 2005).”
Survey on Automated Machine Learning (2019, background): “Finding the global optimum in equations like (1) has been the subject of decades of study (Snyman, 2005).”
Maximum Entropy Deep Inverse Reinforcement Learning (2015, background): “L ( θ ) = log P ( D , θ | r ) = log P ( D| r ) This joint log likelihood is differentiable with respect to the parameters θ of a linear reward model, which allows the application of gradient descent methods (Snyman, 2005).”
Optimal dynamic economic dispatch of generation: A review (2010, methodology): “However, the AI techniques suffer from the drawbacks of the long computation time [74] and the large number of arbitrary or problem-specific parameters [72].”
Decomposition of skin conductance data by means of nonnegative deconvolution (2010, methodology): “The optimization is achieved by means of a gradient descent method (e.g., Snyman, 2005), which essentially changes parameters in direction of highest criterion improvement until no further significant improvement can be obtained.”
Supervised Representation Learning: Transfer Learning with Deep Autoencoders (2015, methodology): “The second way is to apply standard classification algorithms, e.g., logistic regression(LR) [Snyman, 2005; Friedman and Rob, 2010] to train a classifier for source domain in the embedding space.”
Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications (2009, background): “rithm is given by the following dynamics in single dimension [12]:”
Application of reinforcement learning to routing in distributed wireless networks: a review (2013, methodology): “In POMDP-based routing model, a node is not able to clearly observe its operating environment.”
Structural damage detection by a new iterative regularization method and an improved sensitivity function (2017, methodology): “The method of Lagrange multipliers is an equality-constrained optimization problem that attempts to maximize or minimize an objective function with some constraints [30,31].”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, methodology): “All variational schemes use the steepest descent method for minimization of the cost function (Snyman 2005).”
A continuous measure of phasic electrodermal activity (2010, methodology): “The optimization is achieved by means of a gradient descent method (e.g., Snyman, 2005), which essentially changes parameters in direction of highest criterion improvement until no further significant improvement can be obtained.”
Learning Forward Models for the Operational Space Control of Redundant Robots (2010, methodology): “The general form of this solution is written where α is a positive scalar used to tune the steepness of the gradient descent Snyman (2005).”