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Lecture
Optimal Transport: Convex Analysis
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Related lectures (46)
Convex Optimization: Examples of Convex Functions
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Explores convex optimization, convex functions, and their properties, including strict convexity and strong convexity, as well as different types of convex functions like linear affine functions and norms.
Convex Functions: Theory and Applications
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Introduces convex functions, covering affine, convex, and conic hulls, transformations, inequalities, and conditions for convexity.
Conjugate Duality: Envelope Representations and Subgradients
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Explores envelope representations, subgradients, and the duality gap in convex optimization.
Convex Optimization: Theory and Applications
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Explores convex optimization theory, covering convex sets, functions, and QCQP duality.
Convex Optimization: Sets and Functions
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Introduces convex optimization through sets and functions, covering intersections, examples, operations, gradient, Hessian, and real-world applications.
Convex Optimization: Gradient Descent
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Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.
Faster Gradient Descent: Projected Optimization Techniques
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Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.
Optimization Techniques: Gradient Descent and Convex Functions
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Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.
Minkowski-Weyl: Convexity and Separation Theorem
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Explores convex sets, Minkowski-Weyl theorem, and Separation theorem in convex analysis.
Optimal Transport: Theory and Applications
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Explores Lagrange multipliers, minimax theorems, and convex subsets in optimal transport theory.
Proximal Gradient Descent: Optimization Techniques in Machine Learning
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Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.
Convex Optimization Problems: Standard Form
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Covers convex optimization problems, transformation to standard form, and optimality criteria for differentiable objectives.
Optimization Programs: Piecewise Linear Cost Functions
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Covers the formulation of optimization programs for minimizing piecewise linear cost functions.
Optimization Problems: Path Finding and Portfolio Allocation
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Covers optimization problems in path finding and portfolio allocation.
Convex Functions: Theory and Applications
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Untitled
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Linear Models: Continued
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Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.
Optimal Transport: Cyclically Monotone Sets
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Covers cyclically monotone sets in optimal transport theory and their properties.
The Geometry of Linear Optimization
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Convexifying Nonconvex Problems: SVM and Dimensionality Reduction
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Explores convexifying nonconvex problems through SVM and dimensionality reduction techniques.
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