Sebastian Pokutta: A distributed accelerated algorithm for the 1-fair packing problem
The proportional fair resource allocation problem is a major problem studied in flow control of networks, operations research, and economic theory, where it has found numerous applications. This problem, defined as the constrained maximization of ∑_i log x_i, is known as the packing propor
From playlist Workshop: Continuous approaches to discrete optimization
Constrained optimization introduction
See a simple example of a constrained optimization problem and start getting a feel for how to think about it. This introduces the topic of Lagrange multipliers.
From playlist Multivariable calculus
Converting Constrained Optimization to Unconstrained Optimization Using the Penalty Method
In this video we show how to convert a constrained optimization problem into an approximately equivalent unconstrained optimization problem using the penalty method. Topics and timestamps: 0:00 – Introduction 3:00 – Equality constrained only problem 12:50 – Reformulate as approximate unco
From playlist Optimization
Methods for Constrained Local and Global Optimization
Constrained optimization algorithms have been under active development in recent years, with numerous open-source and commercial library solvers emerging for convex, nonconvex, local and global optimization. This talk will cover the Wolfram Language numerical optimization functions for con
From playlist Wolfram Technology Conference 2021
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
Valeria Simoncini: Computational methods for large-scale matrix equations and application to PDEs
Linear matrix equations such as the Lyapunov and Sylvester equations and their generalizations have classically played an important role in the analysis of dynamical systems, in control theory and in eigenvalue computation. More recently, matrix equations have emerged as a natural linear a
From playlist Numerical Analysis and Scientific Computing
Solving Systems of Equations Using the Optimization Penalty Method
In this video we show how to solve a system of equations using numerical optimization instead of analytically solving. We show that this can be applied to either fully constrained or over constrained problems. In addition, this can be used to solve a system of equations that include both
From playlist Optimization
Quentin Berthet: Learning with differentiable perturbed optimizers
Machine learning pipelines often rely on optimization procedures to make discrete decisions (e.g. sorting, picking closest neighbors, finding shortest paths or optimal matchings). Although these discrete decisions are easily computed in a forward manner, they cannot be used to modify model
From playlist Control Theory and Optimization
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
Breaking the Communication-Privacy-Accuracy Trilemma
A Google TechTalk, 2020/7/29, presented by Ayfer Ozgur Aydin, Stanford University ABSTRACT: Two major challenges in distributed learning and estimation are 1) preserving the privacy of the local samples; and 2) communicating them efficiently to a central server, while achieving high accura
From playlist 2020 Google Workshop on Federated Learning and Analytics
“Choice Modeling and Assortment Optimization” - Session I - Prof. Huseyin Topaloglu
This module overviews static and dynamic assortment optimization problems. We start with an introduction to discrete choice modeling and discuss estimation issues when fitting a choice model to observed sales histories. Following this introduction, we discuss static and dynamic assortment
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
Ivan Guo: Stochastic Optimal Transport in Financial Mathematics
Abstract: In recent years, the field of optimal transport has attracted the attention of many high-profile mathematicians with a wide range of applications. In this talk we will discuss some of its recent applications in financial mathematics, particularly on the problems of model calibra
From playlist SMRI Seminars
Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction... - Jeffrey Negrea
Seminar on Theoretical Machine Learning Topic: Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction with Expert Advice Speaker: Jeffrey Negrea Affiliation: University of Toronto Date: July 14, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Your Dreams May Come True with MTP2 by Caroline Uhler
COLLOQUIUM YOUR DREAMS MAY COME TRUE WITH MTP2 SPEAKER: Caroline Uhler (Massachusetts Institute of Technology, Cambridge) DATE: Mon, 22 July 2019, 15:00 to 16:00 VENUE: Emmy Noether Seminar Room, ICTS Campus, Bangalore RESOURCES ABSTRACT We study probability distributions that are m
From playlist ICTS Colloquia
Yusuke Kobayashi: A weighted linear matroid parity algorithm
The lecture was held within the framework of the follow-up workshop to the Hausdorff Trimester Program: Combinatorial Optimization. Abstract: The matroid parity (or matroid matching) problem, introduced as a common generalization of matching and matroid intersection problems, is so gener
From playlist Follow-Up-Workshop "Combinatorial Optimization"
Saharon Rosset: Optimal and maximin procedures for multiple testing problems
CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 05, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
Mirrored Langevin Dynamics - Ya-Ping Hsieh
The workshop aims at bringing together researchers working on the theoretical foundations of learning, with an emphasis on methods at the intersection of statistics, probability and optimization. We consider the posterior sampling problem in constrained distributions, such as the Latent
From playlist The Interplay between Statistics and Optimization in Learning
Phebe Vayanos - Integer optimization for predictive & prescriptive analytics in high stakes domains
Recorded 01 March 2023. Phebe Vayanos of the University of Southern California presents "Integer optimization for predictive and prescriptive analytics in high stakes domains" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Abstract: Motivated by problems in homeless
From playlist 2023 Artificial Intelligence and Discrete Optimization
Stochastic Resetting - CEB T2 2017 - Evans - 3/3
Martin Evans (Edinburgh) - 12/05/2017 Stochastic Resetting We consider resetting a stochastic process by returning to the initial condition with a fixed rate. Resetting is a simple way of generating a nonequilibrium stationary state in the sense that the process is held away from any eq
From playlist 2017 - T2 - Stochastic Dynamics out of Equilibrium - CEB Trimester
Concavity and Parametric Equations Example
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Concavity and Parametric Equations Example. We find the open t-intervals on which the graph of the parametric equations is concave upward and concave downward.
From playlist Calculus