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Simons Institute
Optimization: First-order Methods - Part 1

Explore first-order optimization methods with Alina Ene, focusing on fundamental techniques and algorithms for efficient problem-solving in data structures and algorithm design.
- YouTube
- 57 minutes
- On-Demand
- Free Video
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Optimization Crash Course

Explore advanced optimization techniques and geometric methods for efficient problem-solving in this in-depth lecture by MIT's Ashia Wilson.
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Can Learning Theory Resist Deep Learning? - Francis Bach, INRIA

Exploring global convergence of gradient descent in non-convex optimization for deep learning, highlighting challenges in bridging theory and practice in machine learning algorithms.
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Alan Turing Institute
Optimisation - An Introduction: Professor Coralia Cartis, University of Oxford

Explore optimization techniques, from basic concepts to advanced methods, covering minimizers, derivatives, quadratic functions, and gradient methods for solving complex mathematical problems.
- YouTube
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Simons Institute
Optimization: Higher-order Methods - Part 2

Explore advanced optimization techniques and higher-order methods for fast algorithms, focusing on data structures and their applications in algorithmic efficiency.
- YouTube
- 46 minutes
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Simons Institute
Optimization: Higher-order Methods - Part 1

Explore advanced optimization techniques using higher-order methods, focusing on theoretical foundations and practical applications in algorithm design and data structures.
- YouTube
- 57 minutes
- On-Demand
- Free Video
Simons Institute
Optimization: First-order Methods - Part 2

Explore advanced first-order optimization techniques, focusing on accelerated gradient methods and their applications in solving complex algorithmic problems.
- YouTube
- 1 hour 5 minutes
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Paul G. Allen School
Error Bounds for Conic Feasibility Problems - Case Studies on the Exponential Cone

Explore error bounds for conic feasibility problems, focusing on the exponential cone. Learn about facial reduction and a new framework for deriving error bounds in optimization and data analysis.
- YouTube
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Applied Algebraic Topology Network
Barycenters and a Law of Large Numbers in Gromov Hyperbolic Spaces

Explore barycenters in Gromov hyperbolic spaces, examining contraction properties and a law of large numbers for convex optimization applications in metric spaces.
- YouTube
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Erwin Schrödinger International Institute for Mathematics and Physics (ESI)
Coordinate Descent Methods Beyond Separability and Smoothness

Explore advanced coordinate descent methods for complex optimization problems, covering nonseparable and nonsmooth functions, random proximal gradients, and smooth approximations.
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Erwin Schrödinger International Institute for Mathematics and Physics (ESI)
Progressive Decoupling of Dynamics in Convex Optimal Control

Explore progressive decoupling in convex optimization for optimal control, connecting classical calculus of variations with modern convex analysis and duality concepts.
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International Centre for Theoretical Sciences
Exponentially Faster Gradient Method in the Presence of Ravines

Discover an exponentially faster gradient method designed to navigate optimization ravines efficiently, presented by Damek Davis at ICTS.
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USC Probability and Statistics Seminar
The Dynamics of Iterative Algorithms with Random Data - Beyond First-Order Methods

Explore advanced mathematical techniques for analyzing high-dimensional optimization algorithms, including gradient descent and expectation maximization with rigorous theoretical guarantees.
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Subgradients and Subdifferential

Explore the mathematical concepts of subgradients and subdifferential, essential components in optimization theory and convex analysis.
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Explore the mathematical proof demonstrating why the dual problem in optimization theory exhibits concavity properties.
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