optimization

Various Bounds in Optimization

by ComputingNotes
Regret Bound:https://www.emergentmind.com/topics/cumulative-regret-analysishttp://sbubeck.com/LecturesALL_Bubeck.pdfhttps://www.jmlr.org/papers/volume11/jaksch10a/jaksch10a.pdfhttps://datascience.stackexchange.com/questions/62141/what-are-regret-boundshttps://arxiv.org/pdf/1403.5556 Lecture 1:Introduction to regret analysisS´ebastien BubeckMachine Learning and Optimization group, MSR AI

Assumptions and Their Meaning in Optimization Problems

by ComputingNotes
1. Lipschitz Continuity: The “Speed Limit”This assumption prevents the function from changing too rapidly over a certain distance. 2. 𝐿-Smoothness: The Curvature CeilingSmoothness ensures the gradient (slope) of the function doesn’t change abruptly. A function is 𝐿-smooth if its gradient is Lipschitz continuous. 3. 𝜇-Strong Convexity : The Curvature Floor – Bowl ShapeStrong convexity guarantees that ... Read More

Optimization

by ComputingNotes
Rosenbrock Function $latex f(x,y) = (1-x)^2 + 100(y-x^2)^2$ https://docs.scipy.org/doc/scipy/tutorial/optimize.html
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