Robust optimization is a field of mathematical optimization theory that deals with optimization problems in which a certain measure of robustness is sought against uncertainty that can be represented as deterministic variability in the value of the parameters of the problem itself and/or its solution. (Wikipedia).
13_2 Optimization with Constraints
Here we use optimization with constraints put on a function whose minima or maxima we are seeking. This has practical value as can be seen by the examples used.
From playlist Advanced Calculus / Multivariable Calculus
In this presentation, Paritosh Mokhasi describes robust optimization, a framework for solving optimization problems in the presence of uncertainties. He covers the concept of robust optimization and how the problems are formulated, showing examples that demonstrate how the new Wolfram Lang
From playlist Wolfram Technology Conference 2020
What Is Robust Control? | Robust Control, Part 1
Watch the other videos in this series: Robust Control, Part 2: Understanding Disk Margin - https://youtu.be/XazdN6eZF80 Robust Control, Part 3: Disk Margins for MIMO Systems - https://youtu.be/sac_IYBjcq0 This videos covers a high-level introduction to robust control. The goal is to get
From playlist Robust Control
Robust Design Discovery and Exploration in Bayesian Optimization
A Google TechTalk, presented by Ilija Bogunovic, 2022/10/04 BayesOpt Speaker Series - ABSTRACT: Whether in biological design, causal discovery, material production, or physical sciences, one often faces decisions regarding which new data to collect or which experiments to perform. There is
From playlist Google BayesOpt Speaker Series 2021-2022
Working with Parameter Uncertainty | Robust Control, Part 4
Watch the first videos in this series: Robust Control, Part 1: What Is Robust Control? - https://youtu.be/A7wHSr6GRnc Robust Control, Part 2: Understanding Disk Margin - https://youtu.be/XazdN6eZF80 Robust Control, Part 3: Disk Margins for MIMO Systems - https://youtu.be/sac_IYBjcq0 The
From playlist Robust Control
What in the world is a linear program?
What is a linear program and why do we care? Today I’m going to introduce you to the exciting world of optimization, which is the mathematical field of maximizing or minimizing an objective function subject to constraints. The most fundamental topic in optimization is linear programming,
From playlist Summer of Math Exposition 2 videos
Fast By Default: Algorithmic Performance Optimization in Practice
We’ve learned to rely on sophisticated frameworks and fast engines so much that we’re slowly forgetting how computers work. With modern development tools, it’s easy to locate the exact code that’s slowing down your application, but what do you do next? Why exactly is it slow, and how do yo
From playlist Performance and Testing
This video is #7 in the Adaptive Experimentation series presented at the 18th IEEE Conference on eScience in Salt Lake City, UT (October 10-14, 2022). In this video, Sterling Baird @sterling-baird presents on multiobjective optimization where a pareto front of non-dominated solutions can
From playlist Optimization tutorial
Bartolomeo Stellato - Learning for Decision-Making Under Uncertainty - IPAM at UCLA
Recorded 01 March 2023. Bartolomeo Stellato of Princeton University, Operations Research and Financial Engineering, presents "Learning for Decision-Making Under Uncertainty" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Abstract: We present two data-driven methods t
From playlist 2023 Artificial Intelligence and Discrete Optimization
Jelena Diakonikolas: Local Acceleration of Frank-Wolfe Methods
Conditional gradients (a.k.a. Frank-Wolfe) methods are the convex optimization methods of choice in settings where the feasible set is a convex polytope for which projections are expensive or even computationally intractable, but linear optimization can be implemented efficiently. Unlike p
From playlist Workshop: Continuous approaches to discrete optimization
Stanford Seminar - Opening the Doors of (Robot) Perception - Luca Carlone
Opening the Doors of (Robot) Perception: Towards Certifiable Spatial Perception Algorithms and Systems Luca Carlone MIT February 11, 2022 Spatial perception —the robot’s ability to sense and understand the surrounding environment— is a key enabler for autonomous systems operating in co
From playlist Stanford AA289 - Robotics and Autonomous Systems Seminar
Control Theory and COVID-19: Control Design
Follow on Twitter: https://twitter.com/eigensteve This video will discuss several aspects of the COVID-19 control problem, including model predictive control, robustness, and the challenge of time delays in the system. Website: https://www.eigensteve.com/ Acknowledgements: Consultation
From playlist Control Theory and COVID-19
Control Bootcamp: Introduction to Robust Control
This video motivates robust control with the famous 1978 paper by John Doyle, titled "Guaranteed Margins for LQG Regulators"... Abstract: There are none. Code available at: faculty.washington.edu/sbrunton/control_bootcamp_code.zip These lectures follow Chapters 1 & 3 from: Machine le
From playlist Control Bootcamp
Tradeoffs between Robustness and Accuracy - Percy Liang
More videos on http://video.ias.edu
From playlist Mathematics
Steps Toward Robust Artificial Intelligence: Thomas G Dietterich, Oregon State University
Professor Dietterich is Distinguished Professor (Emeritus) and Director of Intelligent Systems at Oregon State University. He is widely celebrated as one of the founders of machine learning. Among his research contributions were the invention of error-correcting output coding to multi-clas
From playlist AI for Social Good
Optimization in the Wolfram Language
This presentation by Rob Knapp focuses on optimization functionality in the Wolfram Language. Examples are shown to highlight recent progress in convex optimization, including support for complex variables, robust optimization and parametric optimization.
From playlist Wolfram Technology Conference 2020
Monique Laurent: Convergence analysis of hierarchies for polynomial optimization
Minimizing a polynomial f over a region K defined by polynomial inequalities is a hard problem, for which various hierarchies of relaxations have been proposed in the recent years, in particular by Lasserre and Parrilo. These hierarchies are based on using sums of squares representations o
From playlist HIM Lectures: Trimester Program "Combinatorial Optimization"
Optimal control of spin systems with applications in (...) - D. Sugny - Workshop 2 - CEB T2 2018
Dominique Sugny (Univ. Bourgogne) / 05.06.2018 Optimal control of spin systems with applications in Magnetic Resonance Optimal control can be viewed as a generalization of the classical calculus of variations for problems with dynamical constraints. Optimal control was born in its modern
From playlist 2018 - T2 - Measurement and Control of Quantum Systems: Theory and Experiments