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Robust Optimization: Radiation Therapy & Support Vector Machines
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Related lectures (31)
Lagrangian Duality: Convex Optimization
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Explores Lagrangian duality in convex optimization, transforming problems into min-max formulations and discussing the significance of dual solutions.
Convex Optimization: Farkas' Lemma
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Covers Farkas' lemma, exploring the relationship between linear programs and the conditions for its validity.
Convex Sets: Mathematical Optimization
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Introduces convex optimization, covering convex sets, solution concepts, and efficient numerical methods in mathematical optimization.
Conjugate Duality: Envelope Representations and Subgradients
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Explores envelope representations, subgradients, and the duality gap in convex optimization.
Convex Sets: MGT-418 Lecture
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On Convex Optimization covers course organization, mathematical optimization problems, solution concepts, and optimization methods.
Convex Optimization: Gradient Descent
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Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.
Conjugate Duality: Understanding Convex Optimization
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Explores conjugate duality in convex optimization, covering weak and supporting hyperplanes, subgradients, duality gap, and strong duality conditions.
Optimization Techniques: Stochastic Gradient Descent and Beyond
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Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.
Projected Gradient Descent
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Explores convex constrained optimization through Projected Gradient Descent, focusing on tangent space and iterative minimization.
Integer Optimization
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Covers optimization problems, minimal packing, and bounds in Integer Optimization.
Optical Computing Redux: Exploiting Light Scattering for Machine Learning
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Delves into optical computing using light scattering for machine learning tasks, exploring wavefront shaping and computational tools.
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