Convex Functions Courses and Certifications

Institute for Advanced Study

Operator Scaling via Geodesically Convex Optimization, Invariant Theory and Polynomial Identity Testing - Yuanzhi Li

Explore geodesically convex optimization, invariant theory, and polynomial identity testing in this advanced seminar on operator scaling, featuring key theorems and proofs.

• YouTube
• 1 hour 41 minutes
• On-Demand
• Free Video

Convex Optimization

7 reviews

Explore convex optimization techniques for engineering and scientific applications, covering theory, analysis, and practical problem-solving in various fields like signal processing and machine learning.

• edX
• 8 weeks, 10-15 hours a week
• On-Demand
• Free Online Course (Audit)

Hausdorff Center for Mathematics

An Overview on a Young Research Topic - Valuations on Spaces of Functions

Explore valuations on function spaces, from convex bodies to Lipschitz and convex functions. Learn about recent classifications and advancements in this emerging mathematical field.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Hausdorff Center for Mathematics

Geometric Probabilities and Valuation Theory

Explore applications of geometric valuation theory to probabilities and integral geometry, including Cauchy-Kubota formulas and recent findings for convex functions.

• YouTube
• 38 minutes
• On-Demand
• Free Video

International Mathematical Union

Enlargements - A Bridge Between Maximal Monotonicity and Convexity

Explore connections between maximally monotone operators and convex functions through enlargements, offering insights into variational problems and new distance measures for point-to-set maps.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Association for Computing Machinery (ACM)

A Lower Bound for Parallel Submodular Minimization

Explore lower bounds for parallel submodular minimization, examining adaptive complexity models and exponentially faster algorithms for this optimization problem.

• YouTube
• 22 minutes
• On-Demand
• Free Video

Convex Optimization I - 2023

Master convex optimization fundamentals and applications across engineering, finance, and machine learning through Stanford's comprehensive mathematical framework and solution techniques.

• YouTube
• 23 hours 44 minutes
• On-Demand
• Free Video

Hausdorff Center for Mathematics

Vysotskiy - The Isoperimetric Problem for Convex Hulls and the Large Deviations Rate Functions

Explores large deviations principle for convex hull areas in planar random walks, analyzing optimal trajectories and solving the Euler-Lagrange equation for various rate functions.

• YouTube
• 28 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Convex Hull Approximation for Activation Functions

Discover WraAct, a novel approach for efficiently constructing tight over-approximations of activation function convex hulls to enhance neural network verification precision and speed.

• YouTube
• 14 minutes
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Stochastic Gradient Descent and Machine Learning - Lecture 1

Explore optimization methods, gradient descent, Newton's method, and convexity in machine learning. Gain insights into iterative algorithms and their applications in statistical physics and data analysis.

• YouTube
• 1 hour 53 minutes
• On-Demand
• Free Video

Paul Hand

Gradient Descent and Stochastic Gradient Descent

Explore gradient descent and stochastic gradient descent in deep neural networks, covering convergence rates, learning rate effects, and challenges in optimization algorithms.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Hausdorff Center for Mathematics

Hajime Ishihara - The Constructive Hahn Banach Theorem, Revisited

Explores constructive versions of the Hahn-Banach theorem, focusing on separation and extension theorems. Discusses geometric properties, subderivatives, and Gâteaux derivatives in functional analysis.

• YouTube
• 33 minutes
• On-Demand
• Free Video

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Nonmonotone Forward-Backward Splitting Method for Infinite-Dimensional Optimization Problems

Explore nonmonotone forward-backward splitting for nonsmooth composite problems in Hilbert spaces, covering convergence analysis, complexity, and numerical experiments.

• YouTube
• 23 minutes
• On-Demand
• Free Video

Centre for Networked Intelligence, IISc

Optimal Algorithms for Online Convex Optimization with Adversarial Constraints

Discover optimal algorithms for online convex optimization with adversarial constraints, exploring how to achieve small regret and constraint violation simultaneously through Lyapunov optimization and AdaGrad techniques.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

NPTEL-NOC IITM

Subgradients and Subdifferential

Explore the mathematical concepts of subgradients and subdifferential, essential components in optimization theory and convex analysis.

• YouTube
• 33 minutes
• On-Demand
• Free Video