Understanding quantum algorithms via query complexity – Andris Ambainis – ICM2018
Mathematical Aspects of Computer Science Invited Lecture 14.2 Understanding quantum algorithms via query complexity Andris Ambainis Abstract: Query complexity is a model of computation in which we have to compute a function f(x_1, …, x_N) of variables x_i which can be accessed via querie
From playlist Mathematical Aspects of Computer Science
A very basic overview of optimization, why it's important, the role of modeling, and the basic anatomy of an optimization project.
From playlist Optimization
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
Results and open problems in theory of quantum complexity - Anindya De
Andris Ambainis University of Latvia; Member, School of Mathematics April 22, 2014 I will survey recent results and open problems in several areas of quantum complexity theory, with emphasis on open problems which can be phrased in terms of classical complexity theory or mathematics but ha
From playlist Mathematics
Calculus: Optimization Problems
In this video, I discuss optimization problems. I give an outline for how to approach these kinds of problems and worth through a couple of examples.
From playlist Calculus
Big O Notation: A Few Examples
This video is about Big O Notation: A Few Examples Time complexity is commonly estimated by counting the number of elementary operations (elementary operation = an operation that takes a fixed amount of time to preform) performed in the algorithm. Time complexity is classified by the nat
From playlist Computer Science and Software Engineering Theory with Briana
13_1 An Introduction to Optimization in Multivariable Functions
Optimization in multivariable functions: the calculation of critical points and identifying them as local or global extrema (minima or maxima).
From playlist Advanced Calculus / Multivariable Calculus
Lower Bound on Complexity - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Yin Tat Lee & Aaron Sidford: Faster Cutting Plane Methods and Improved Running Times for Submodular
Yin Tat Lee & Aaron Sidford: Faster Cutting Plane Methods and Improved Running Times for Submodular Function Minimization In this talk we will present a new cutting plane method and show how this technique can be used to achieve faster asymptotic running times for fundamental problems in
From playlist HIM Lectures 2015
Optimization in Theory and Practice
Stephen Wright, University of Wisconsin-Madison, USA
From playlist Distinguished Visitors Lecture Series
Approximating Max Cut with Subexponential Linear Programs - Tselil Schramm
Computer Science/Discrete Mathematics Seminar I Topic: Approximating Max Cut with Subexponential Linear Programs Speaker: Tselil Schramm Affiliation: Stanford University Date: March 29, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
Haotian Jiang: Minimizing Convex Functions with Integral Minimizers
Given a separation oracle SO for a convex function f that has an integral minimizer inside a box with radius R, we show how to find an exact minimizer of f using at most • O(n(n + log(R))) calls to SO and poly(n,log(R)) arithmetic operations, or • O(nlog(nR)) calls to SO and exp(O(n)) · po
From playlist Workshop: Continuous approaches to discrete optimization
Stochastic Gradient Descent and Machine Learning (Lecture 1) by Praneeth Netrapalli
PROGRAM: BANGALORE SCHOOL ON STATISTICAL PHYSICS - XIII (HYBRID) ORGANIZERS: Abhishek Dhar (ICTS-TIFR, India) and Sanjib Sabhapandit (RRI, India) DATE & TIME: 11 July 2022 to 22 July 2022 VENUE: Madhava Lecture Hall and Online This school is the thirteenth in the series. The schoo
From playlist Bangalore School on Statistical Physics - XIII - 2022 (Live Streamed)
Csaba Szepesvari: "Model misspecification in reinforcement learning"
Intersections between Control, Learning and Optimization 2020 "Model misspecification in reinforcement learning" Csaba Szepesvari - University of Alberta Abstract: Model misspecification refers to that the assumed model class used in a learning/reasoning algorithm represents an imperfect
From playlist Intersections between Control, Learning and Optimization 2020
Introductory lectures on first-order convex optimization (Lecture 1) by Praneeth Netrapalli
DISCUSSION MEETING : STATISTICAL PHYSICS OF MACHINE LEARNING ORGANIZERS : Chandan Dasgupta, Abhishek Dhar and Satya Majumdar DATE : 06 January 2020 to 10 January 2020 VENUE : Madhava Lecture Hall, ICTS Bangalore Machine learning techniques, especially “deep learning” using multilayer n
From playlist Statistical Physics of Machine Learning 2020
Ben Adcock: Compressed sensing and high-dimensional approximation: progress and challenges
Abstract: Many problems in computational science require the approximation of a high-dimensional function from limited amounts of data. For instance, a common task in Uncertainty Quantification (UQ) involves building a surrogate model for a parametrized computational model. Complex physica
From playlist Numerical Analysis and Scientific Computing
A Framework for Quadratic Form Maximization over Convex Sets -Vijay Bhattiprolu
Computer Science/Discrete Mathematics Seminar II Topic: A Framework for Quadratic Form Maximization over Convex Sets Speaker: Vijay Bhattiprolu Affiliation: Member, School of Mathematics Date: April 28, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Algorithms Explained: Computational Complexity
An overview of computational complexity including the basics of big O notation and common time complexities with examples of each. Understanding computational complexity is vital to understanding algorithms and why certain constructions or implementations are better than others. Even if y
From playlist Algorithms Explained
PAC-Bayesian approaches to understanding generalization in deep learning - Gintare Dziugaite
Workshop on Theory of Deep Learning: Where next? Topic: PAC-Bayesian approaches to understanding generalization in deep learning Speaker: Gintare Karolina Dziugaite Affiliation:Simons Institute for the Theory of Computing Date: October 15, 2019 For more video please visit http://video.i
From playlist Mathematics
Computational Complexity in Mechanism Design - Jing Chen
Jing Chen Massachusetts Institute of Technology; Member, School of Mathematics November 27, 2012 Some important mechanisms considered in game theory require solving optimization problems that are computationally hard. Solving these problems approximately may not help, as it may change the
From playlist Mathematics