Interval scheduling is a class of problems in computer science, particularly in the area of algorithm design. The problems consider a set of tasks. Each task is represented by an interval describing the time in which it needs to be processed by some machine (or, equivalently, scheduled on some resource). For instance, task A might run from 2:00 to 5:00, task B might run from 4:00 to 10:00 and task C might run from 9:00 to 11:00. A subset of intervals is compatible if no two intervals overlap on the machine/resource. For example, the subset {A,C} is compatible, as is the subset {B}; but neither {A,B} nor {B,C} are compatible subsets, because the corresponding intervals within each subset overlap. The interval scheduling maximization problem (ISMP) is to find a largest compatible set, i.e., a set of non-overlapping intervals of maximum size. The goal here is to execute as many tasks as possible, that is, to maximize the throughput. It is equivalent to finding a maximum independent set in an interval graph. A generalization of the problem considers machines/resources. Here the goal is to find compatible subsets whose union is the largest. In an upgraded version of the problem, the intervals are partitioned into groups. A subset of intervals is compatible if no two intervals overlap, and moreover, no two intervals belong to the same group (i.e., the subset contains at most a single representative of each group). Each group of intervals corresponds to a single task, and represents several alternative intervals in which it can be executed. The group interval scheduling decision problem (GISDP) is to decide whether there exists a compatible set in which all groups are represented. The goal here is to execute a single representative task from each group. GISDPk is a restricted version of GISDP in which the number of intervals in each group is at most k. The group interval scheduling maximization problem (GISMP) is to find a largest compatible set - a set of non-overlapping representatives of maximum size. The goal here is to execute a representative task from as many groups as possible. GISMPk is a restricted version of GISMP in which the number of intervals in each group is at most k. This problem is often called JISPk, where J stands for Job. GISMP is the most general problem; the other two problems can be seen as special cases of it: * ISMP is the special case in which each task belongs to its own group (i.e. it is equal to GISMP1). * GISDP is the problem of deciding whether the maximum exactly equals the number of groups. All these problems can be generalized by adding a weight for each interval, representing the profit from executing the task in that interval. Then, the goal is to maximize the total weight. All these problems are special cases of single-machine scheduling, since they assume that all tasks must run on a single processor. Single-machine scheduling is a special case of optimal job scheduling. (Wikipedia).
This lesson introduces the topic of scheduling and define basic scheduling vocabulary. Site: http://mathispower4u.com
From playlist Scheduling
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
Scheduling: The Decreasing Time Algorithm
This lesson explains how to use the decreasing time algorithm to create a priority list and then a schedule. Site: http://mathispower4u.com
From playlist Scheduling
An animation showing the main features of a process scheduling system including the ready queue, blocked queue, high level scheduler and low level scheduler. It explains the principle of a round robin scheduling algorithm.
From playlist Operating Systems
Into to the Mathematics of Scheduling
Terminology explained includes preference schedule, digraphs, tasks, arcs, processors, and timelines.
From playlist Discrete Math
Scheduling: The List Processing Algorithm Part 2
This lesson explains and provides an example of the list processing algorithm to create a digraph and make a schedule. Site: http://mathispower4u.com
From playlist Scheduling
The way how to show time using clocks. It is 12 hours video you can use as a screensaver on clock, every number changing is completely random. Please enjoy.
From playlist Timers
Thomas Rothvoß: Scheduling with Communication Delays via LP Hierarchies and Clustering
We consider the classic problem of scheduling jobs with precedence constraints on identical machines to minimize makespan, in the presence of communication delays. In this setting, denoted by P | prec,c | Cmax, if two dependent jobs are scheduled on different machines, then at least c unit
From playlist Workshop: Approximation and Relaxation
Time Management Tutorial - Tips on scheduling meetings
Learn tips and best practices for scheduling a meeting. Explore more Time Management courses and advance your skills on LinkedIn Learning: https://www.linkedin.com/learning/topics/time-management-3?trk=sme-youtube_M140599-20-03_learning&src=yt-other This is an excerpt from "Time Managemen
From playlist Time Management
Operant conditioning: Schedules of reinforcement | Behavior | MCAT | Khan Academy
Created by Jeffrey Walsh. Watch the next lesson: https://www.khanacademy.org/test-prep/mcat/behavior/learning-slug/v/operant-conditioning-innate-vs-learned-behaviors?utm_source=YT&utm_medium=Desc&utm_campaign=mcat Missed the previous lesson? https://www.khanacademy.org/test-prep/mcat/beh
From playlist Behavior | MCAT | Khan Academy
SimBiology Tutorials: Working with Doses in SimBiology
Create and apply dose schedules to a model in SimBiology®. -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: http
From playlist SimBiology Tutorials for QSP, PBPK, and PK/PD Modeling and Analysis
Psych9B. Psychology Fundamentals. Lecture 7:
UCI Psych 9B: Psych Fundamentals (Fall 2015) Lec 07. Psych Fundamentals View the complete course: http://ocw.uci.edu/courses/psych_9bpsy_beh_11b_psychology_fundamentals.html Instructor: Mark Steyvers, Ph.D. License: Creative Commons CC-BY-SA Terms of Use: http://ocw.uci.edu/info. More cou
From playlist Psych 9B: Psych Fundamentals
R1. Matrix Multiplication and the Master Theorem
MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: http://ocw.mit.edu/6-046JS15 Instructor: Ling Ren In this recitation, problems related to matrix multiplication and weighted interval scheduling are discussed. Chapters 00:00 Title slate 00:20 Recitation
From playlist MIT 6.046J Design and Analysis of Algorithms, Spring 2015
I asked ChatGPT how to study with Spaced Repetition
https://memorycourse.brainathlete.com/memorytips Get my memory training tips at the links above Spaced repetition is a powerful learning technique that involves revisiting material at gradually increasing intervals to improve long-term retention. Here's a step-by-step guide on how to use
From playlist How to Study
Shark Attack! Thursday at Noon - Variable Ratio & Interval Schedules - Extra Credits
Skinner boxes are fairly infamous in the game space at this point, but not all boxes are created equal. Why do some games grip and hold onto our attention but some games just end up falling by the wayside, despite having great graphics, gripping narratives, or engaging gameplay? Why do som
From playlist Extra Credits: Game Design
Puzzle 2: The Best Time to Party
MIT 6.S095 Programming for the Puzzled, IAP 2018 View the complete course: https://ocw.mit.edu/6-S095IAP18 Instructor: Srini Devadas Ever wanted to go to a party at just the right time so you can hang out with all the cool people? Prof. Devadas describes an efficient, algorithmic way of f
From playlist MIT 6.S095 Programming for the Puzzled, January IAP 2018
MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: http://ocw.mit.edu/6-046JS15 Instructor: Amartya Shankha Biswas In this recitation, problems related to greedy algorithms are discussed. License: Creative Commons BY-NC-SA More information at http://ocw.m
From playlist MIT 6.046J Design and Analysis of Algorithms, Spring 2015
Searching and Sorting Algorithms (part 4 of 4)
Introductory coverage of basic searching and sorting algorithms, as well as a rudimentary overview of Big-O algorithm analysis. Part of a larger series teaching programming at http://codeschool.org
From playlist Searching and Sorting Algorithms