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matbesancon.xyz | ||
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jeremykun.wordpress.com
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| | | | | This post is a sequel toFormulating the Support Vector Machine Optimization Problem. The Karush-Kuhn-Tucker theorem Generic optimization problems are hard to solve efficiently. However, optimization problems whose objective and constraints have special structureoften succumb to analytic simplifications. For example, if you want to optimize a linear function subject to linear equality constraints, one can compute... | |
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liorsinai.github.io
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| | | | | A series on automatic differentiation in Julia. Part 1 provides an overview and defines explicit chain rules. | |
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www.jeremykun.com
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| | | | | This post is a sequel to Formulating the Support Vector Machine Optimization Problem. The Karush-Kuhn-Tucker theorem Generic optimization problems are hard to solve efficiently. However, optimization problems whose objective and constraints have special structure often succumb to analytic simplifications. For example, if you want to optimize a linear function subject to linear equality constraints, one can compute the Lagrangian of the system and find the zeros of its gradient. More generally, optimizing... | |
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christopher-beckham.github.io
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| | | Vicinal distributions as a statistical view on data augmentation | ||
