Basic stochastic simulation b: Stochastic simulation algorithm
(C) 2012-2013 David Liao (lookatphysics.com) CC-BY-SA Specify system Determine duration until next event Exponentially distributed waiting times Determine what kind of reaction next event will be For more information, please search the internet for "stochastic simulation algorithm" or "kin
From playlist Probability, statistics, and stochastic processes
Jana Cslovjecsek: Efficient algorithms for multistage stochastic integer programming using proximity
We consider the problem of solving integer programs of the form min {c^T x : Ax = b; x geq 0}, where A is a multistage stochastic matrix. We give an algorithm that solves this problem in fixed-parameter time f(d; ||A||_infty) n log^O(2d) n, where f is a computable function, d is the treed
From playlist Workshop: Parametrized complexity and discrete optimization
Stochastic Approximation-based algorithms, when the Monte (...) - Fort - Workshop 2 - CEB T1 2019
Gersende Fort (CNRS, Univ. Toulouse) / 13.03.2019 Stochastic Approximation-based algorithms, when the Monte Carlo bias does not vanish. Stochastic Approximation algorithms, whose stochastic gradient descent methods with decreasing stepsize are an example, are iterative methods to comput
From playlist 2019 - T1 - The Mathematics of Imaging
This lesson introduces the topic of scheduling and define basic scheduling vocabulary. Site: http://mathispower4u.com
From playlist Scheduling
"Data-Driven Optimization in Pricing and Revenue Management" by Arnoud den Boer - Lecture 1
In this course we will study data-driven decision problems: optimization problems for which the relation between decision and outcome is unknown upfront, and thus has to be learned on-the-fly from accumulating data. This type of problems has an intrinsic tension between statistical goals a
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
Scheduling: The List Processing Algorithm Part 1
This lesson explains and provides an example of the list processing algorithm to make a schedule given a priority list. Site: http://mathispower4u.com
From playlist Scheduling
Viswanath Nagarajan: Stochastic load balancing on unrelated machines
The lecture was held within the framework of the follow-up workshop to the Hausdorff Trimester Program: Combinatorial Optimization. Abstract: We consider the unrelated machine scheduling problem when job processing-times are stochastic. We provide the first constant factor approximation
From playlist Follow-Up-Workshop "Combinatorial Optimization"
“Choice Modeling and Assortment Optimization” – Session III – Prof. Huseyin Topaloglu
This module overviews static and dynamic assortment optimization problems. We will 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 will discuss static and dynamic a
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
Stochastic Gradient Descent: where optimization meets machine learning- Rachel Ward
2022 Program for Women and Mathematics: The Mathematics of Machine Learning Topic: Stochastic Gradient Descent: where optimization meets machine learning Speaker: Rachel Ward Affiliation: University of Texas, Austin Date: May 26, 2022 Stochastic Gradient Descent (SGD) is the de facto op
From playlist Mathematics
Bao Wang: "Momentum in Stochastic Gradient Descent and Deep Neural Nets"
Deep Learning and Medical Applications 2020 "Momentum in Stochastic Gradient Descent and Deep Neural Nets" Bao Wang - University of California, Los Angeles (UCLA), Mathematics Abstract: Stochastic gradient-based optimization algorithms play perhaps the most important role in modern machi
From playlist Deep Learning and Medical Applications 2020
Real-Space Stochastic GW Calculations Benchmark on GW100 by Ishita Shitut
DISCUSSION MEETING : APS SATELLITE MEETING AT ICTS ORGANIZERS : Ranjini Bandyopadhyay (RRI, India), Subhro Bhattacharjee (ICTS-TIFR, India), Arindam Ghosh (IISc, India), Shobhana Narasimhan (JNCASR, India) and Sumantra Sarkar (IISc, India) DATE & TIME: 15 March 2022 to 18 March 2022 VEN
From playlist APS Satellite Meeting at ICTS-2022
Artificial Intelligence & Machine Learning 4 - Stochastic Gradient Descent | Stanford CS221 (2021)
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021
Lookahead Optimizer: k steps forward, 1 step back
Speaker/author: Michael Zhang For details including paper and slides, please visit https://aisc.ai.science/events/2019-09-22-lookahead-optimizer
From playlist Math and Foundations
MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT RES.6-012 Introduction to Probability, Spring 2018
AQC 2016 - Coupled Quantum Fluctuations and Quantum Annealing
A Google TechTalk, June 29, 2016, presented by Layla Hormozi (MIT) ABSTRACT: We study the relative effectiveness of stoquastic and non-stoquastic Hamiltonians consisting of coupled quantum fluctuations compared to Hamiltonians with single spin flips in the performance of quantum annealing.
From playlist Adiabatic Quantum Computing Conference 2016
Louis-Martin Rousseau - Combining Machine Learning & Optimization for efficient healthcare delivery
Recorded 01 March 2023. Louis-Martin Rousseau of École Polytechnique de Montréal presents "Combining Machine Learning and Optimization for efficient healthcare delivery" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Learn more online at: http://www.ipam.ucla.edu/pro
From playlist 2023 Artificial Intelligence and Discrete Optimization
Summary Discussion: Experimental Edition by Stan Whitcomb
Discussion Meeting The Future of Gravitational-Wave Astronomy ORGANIZERS: Parameswaran Ajith, K. G. Arun, B. S. Sathyaprakash, Tarun Souradeep and G. Srinivasan DATE: 19 August 2019 to 22 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore This discussion meeting, organized in c
From playlist The Future of Gravitational-wave Astronomy 2019
“Choice Modeling and Assortment Optimization” - Session II - Prof. Huseyin Topaloglu
This module overviews static and dynamic assortment optimization problems. We will 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 will discuss static and dynamic a
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management