Lecture
Mathematics of Data: Computation Role
In course
DEMO: mollit minim ullamco est
Ex mollit laborum et tempor aliquip consectetur ea ut dolore ullamco exercitation. Qui nulla excepteur elit sit adipisicing fugiat sint dolore. Id deserunt voluptate deserunt veniam elit reprehenderit ipsum exercitation ullamco ipsum et. Irure et pariatur nostrud aute aliquip consequat pariatur deserunt excepteur pariatur. Sint commodo amet incididunt eu proident.
Description
This lecture by the instructor covers the role of computation in the mathematics of data, focusing on iterative descent methods, unconstrained minimization, maximum-likelihood estimators, approximate vs. exact optimality, and the basic principles of descent methods. The lecture delves into the challenges faced by iterative optimization algorithms, the conditions for local descent directions, and the basic iterative strategy for optimization algorithms. It also discusses the concepts of local steepest descent direction, stationarity, and the geometric interpretation of descent methods.
Instructor
excepteur ex cillum enim
Cupidatat adipisicing elit velit elit minim magna dolor id enim velit. Nisi consequat voluptate velit exercitation consequat proident. Eiusmod deserunt ad irure dolor quis anim pariatur cupidatat do aute ad eu. Eiusmod irure occaecat qui deserunt ullamco ipsum aliquip adipisicing. Est irure nostrud reprehenderit irure magna.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (49)
Gradient Descent Methods: Theory and Computation
Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.
Optimality of Convergence Rates: Accelerated Gradient Descent
Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.
Optimization Methods: Theory Discussion
Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.
Primal-dual Optimization: Extra-Gradient Method
Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.
Nonlinear Programming: Part I
Covers the fundamentals of Nonlinear Programming and its applications in Optimal Control, exploring techniques, examples, optimality definitions, and necessary conditions.