Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Riemannian Gradient Descent: Convergence Theorem and Line Search Method
Graph Chatbot
Related lectures (57)
Optimization on Manifolds
Log in to Mediaspace to watch this video
Covers optimization on manifolds, focusing on smooth manifolds and functions, and the process of gradient descent.
Riemannian distance, geodesically convex sets
Log in to Mediaspace to watch this video
Covers the structure of Riemannian manifolds, geodesic convexity, and the Riemannian distance function.
Newton's method on Riemannian manifolds
Log in to Mediaspace to watch this video
Covers Newton's method on Riemannian manifolds, focusing on second-order optimality conditions and quadratic convergence.
Symmetry Property: Riemannian Connection in Geometry
Log in to Mediaspace to watch this video
Explores symmetries, Riemannian connection, vector fields, and Lie bracket in geometry.
Gradients on Riemannian submanifolds, local frames
Log in to Mediaspace to watch this video
Discusses gradients on Riemannian submanifolds and the construction of local frames.
All things Riemannian: metrics, (sub)manifolds and gradients
Log in to Mediaspace to watch this video
Covers the definition of retraction, open submanifolds, local defining functions, tangent spaces, and Riemannian metrics.
The Conjugate Gradients Method (CG)
Log in to Mediaspace to watch this video
Covers the Conjugate Gradients method for solving linear systems iteratively with quadratic convergence and emphasizes the importance of linear independence among conjugate directions.
Riemannian Trust Regions framework
Log in to Mediaspace to watch this video
Introduces the Riemannian Trust Regions (RTR) framework, covering conjugate directions, Newton's method, and model improvement.
Single Inequality or Equality Constraint
Log in to Mediaspace to watch this video
Covers single inequality or equality constraints and necessary optimality conditions in optimization problems.
Optimization algorithms
Log in to Mediaspace to watch this video
Covers optimization algorithms, focusing on Proximal Gradient Descent and its variations.
Variance Reduction: Strategies and Applications
Log in to Mediaspace to watch this video
Discusses variance reduction techniques in stochastic simulation, focusing on allocation strategies and replica generation algorithms.
Optimization Principles
Log in to Mediaspace to watch this video
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Non-analytic Smooth Functions
Log in to Mediaspace to watch this video
Explores non-analytic smooth functions, their properties, and applications in differential geometry and partitioning unity.
Semi-Definite Programming
Log in to Mediaspace to watch this video
Covers semi-definite programming and optimization over positive semidefinite cones.
Dynamic Programming: How Many Ways to Make Change
Log in to Mediaspace to watch this video
Demonstrates dynamic programming to find the number of ways to make change using different coin denominations.
Proximal Gradient Descent: Optimization Techniques in Machine Learning
Log in to Mediaspace to watch this video
Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.
Initial BFS: Finding Solutions
Log in to Mediaspace to watch this video
Covers the concept of finding an initial BFS and solving related optimization problems.
Extreme Values and Optimization
Log in to Mediaspace to watch this video
Covers extreme values, optimization conditions, feasible sets, and partition formation for optimization.
Dynamic Programming: Steinitz Sequence
Log in to Mediaspace to watch this video
Explores dynamic programming with the Steinitz sequence to optimize solutions efficiently.
Faster Gradient Descent: Projected Optimization Techniques
Log in to Mediaspace to watch this video
Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.
Previous
Page 2 of 3
Next