Moving Target is the début album by the band Moving Target, headed by frontman Simon Townshend, the younger brother of The Who's guitarist Pete Townshend. The album, released by the label Polydor Records in the year 1985, Moving Target was Moving Target's only release, and was a commercial failure. It also marked the end of the career for the band, but is often counted among Townshend's discography, sometimes credited as a solo effort (since the band Moving Target formed and disbanded around this single effort).
The album was produced by the infamous Neil Kernon who is a Grammy Award Winning producer, and has made over 40 Gold and Platinum records under his name. This album is one of three albums produced by Kernon in the year 1985.
The band "Moving Target" features Simon Townshend playing guitar, Andy Shillito playing bass guitar, Dane Morrell playing drums and Paul Rogers playing keyboards, The band once played live at the rock club The Ritz in New York on which they played the tracks "Meet You", "Frustrated Hearts", "Addictions", and "Genuine" from this album as well as "I'm the Answer" from Sweet Sound and a new track "Broken Heart" which wasn't recorded until 1987 when it was released as a single.
A second record was to have been released on the label Atlantic Records, but the label kept Simon Townshend busy remixing and re-recording under their contract, they never seemed pleased with the results. Once that the contract was over, Simon Townshend reformed "On the Air". only this time instead of Tony Butler, he recruited Andy Shilito, the bassist he had worked with on his earlier album's.
In graph theory, a flow network (also known as a transportation network) is a directed graph where each edge has a capacity and each edge receives a flow. The amount of flow on an edge cannot exceed the capacity of the edge. Often in operations research, a directed graph is called a network. The vertices are called nodes and the edges are called arcs. A flow must satisfy the restriction that the amount of flow into a node equals the amount of flow out of it, unless it is a source, which has only outgoing flow, or sink, which has only incoming flow. A network can be used to model traffic in a road system, circulation with demands, fluids in pipes, currents in an electrical circuit, or anything similar in which something travels through a network of nodes.
Definition
Let be a finite directed graph in
which every edge has a non-negative, real-valued capacity . If , we assume that . We distinguish two vertices: a source s and a sink t. A flow in a flow network is a realfunction with the following three properties for all nodes u and v:
Network is a real-time, two playerbusiness simulation game developed by David Mullich for the Apple II in 1980. Two players play competitively against the computer, each taking the role of the programming chief for a major television network. Each side bids on new television shows to add to the season’s line-up, schedules them, monitors the weekly ratings, and then drops shows with poor ratings or reschedules them to recover from mistakes at the end of the thirteen week season. The side with the highest ratings is the winner.
References
Product Catalog. Edu-Ware Services. August 1, 1980.
Network Ten (commonly known as Channel Ten or simply Ten, stylised as TEN), is an Australian commercial broadcasting network owned by Ten Network Holdings, and is headquartered in Pyrmont, New South Wales. It is the third Australian free-to-airtelevision network to make its debut after Nine Network in 1956 and Seven Network in 1956. It first aired on August 1, 1964 in Melbourne. Network Ten is the fourth of all the five national free-to-air networks in Australia, after the ABC. Its owned-and-operated stations can be found in the capital cities of Sydney, Melbourne, Brisbane, Adelaide and Perth, while affiliates extend the network to cover most of the country. The channel's name became a backronym for "The Entertainment Network" during the early-1990s.
History
Origins
From the introduction of TV in 1956 until 1965 there were only two commercial television networks in Australia, the National Television Network (now the Nine Network) and the Australian Television Network (now the Seven Network), as well as the public Australian Broadcasting Corporation (then Commission). In the early 1960s, the federal government began canvassing the idea of licensing a third commercial television station in each capital city. This decision was seen by some as a way for the government to defuse growing public dissatisfaction with the dominance of imported overseas programming and the paucity of local content. The first of these "third" licences was granted to United Telecasters Sydney was granted on 4 April 1963.
M.O.V.I.E is set in New York in the 1930s. The player takes the role of Jack Marlow, a private investigator who must enter the headquarters of mob boss Bugs Malloy in order to retrieve an audiotape. In order to help him complete this task, Marlow needs the help of a girl called Tanya. Unfortunately, she has an identical twin called Vanya who is allied to Malloy and who will deliberately lead him into trouble.
Gameplay
The game is an arcade adventure which uses an isometric display to portray the action. Movement is achieved by rotating the main character and moving him forward, similar to that of Knightlore and other early examples of the genre. The player can also access a panel of icons which allow Marlow to carry out certain actions such as dropping and taking items, shooting his firearm, punching, throwing an item or talking. The latter is performed using speech-bubbles in which the player can type out words and phrases using the keyboard.
April 14 - The first commercial presentation of the Kinetoscope took place in the Holland Brothers' Kinetoscope Parlor at 1155 Broadway, New York City.
June 6 - Charles Francis Jenkins projects a filmed motion picture before an audience in Richmond, Indiana. Earliest documented projection of a motion picture.
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud061
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
published: 23 Feb 2015
Ford-Fulkerson in 5 minutes
Step by step instructions showing how to run Ford-Fulkerson on a flow network.
Code: https://github.com/msambol/dsa/blob/master/maximum_flow/ford_fulkerson.py
Source: http://www.win.tue.nl/~nikhil/courses/2WO08/07NetworkFlowI.pdf
LinkedIn: https://www.linkedin.com/in/michael-sambol
published: 08 Jul 2015
Max Flow Ford Fulkerson | Network Flow | Graph Theory
Explanation of how to find the maximum flow with the Ford-Fulkerson method
Next video: https://youtu.be/Xu8jjJnwvxE
Algorithms repository:
https://github.com/williamfiset/algorithms#network-flow
Video slides:
https://github.com/williamfiset/Algorithms/tree/master/slides
0:00 Intro and motivation for maximum flow
2:42 Basics and definitions of network flow concepts
4:26 Augmenting paths, residual edges and the residual graph
8:05 Ford-Fulkerson with DFS example
9:49 Ford-Fulkerson time complexity
11:22 Faster network flow algorithms
======================================
Practicing for interviews? I have used, and recommend `Cracking the Coding Interview` which got me a job at Google. Link on Amazon: https://amzn.to/3cvMof5
A lot of the content on this channel is inspired by the book...
published: 29 Aug 2018
Finding maximum flow through a network
This is an alternative to the minimum cut/maximum flow theorem to find the maximum flow through a network. It seems more intuitive and less error-prone than trying to list all cuts and find the capacity of each one.
Free 5-Day Mini-Course: https://backtobackswe.com
Try Our Full Platform: https://backtobackswe.com/pricing
📹 Intuitive Video Explanations
🏃 Run Code As You Learn
💾 Save Progress
❓New Unseen Questions
🔎 Get All Solutions
Flow Networks: https://en.wikipedia.org/wiki/Flow_network
Ford–Fulkerson Algorithm: https://en.wikipedia.org/wiki/Ford%E2%80%93Fulkerson_algorithm
Max-Flow Min-Cut Theorem: https://en.wikipedia.org/wiki/Max-flow_min-cut_theorem
Proofs: Reference "Algorithm Design" by Jon Kleinberg and Éva Tardos Chapters 7.1, 7.2 for excellent proofs on all of this.
Things I'd Improve On This Explanation (w/ More Time):
1.) I should have done a walk-through showing how the residual graph dictates how the original graph's edge flows (f(e)) are updated each iteration. (That would...
published: 28 Oct 2019
Maximum flow problem - Ford Fulkerson algorithm
To create this video, I used a library for Manim that I have been developing for some months.
https://verdianapasqualini.github.io/GraphLibrary/
The developing of ManimGraphLibrary is part of my master thesis in Mathematics for Data Science @ University of Trento, Italy.
Summer of Math Exposition challenged me to produce a full video, record the voiceover and publish it instead of just working on the library.
published: 19 Aug 2021
#7 Maximal flow problem in Tamil
Design and analysis of alg playlist link:
https://youtube.com/playlist?list=PL1eBtGPYeYXXky8-3Frs02CyD0-Xy359M
published: 28 Nov 2019
DM 01 Max Flow and Min Cut Theorem Transport Network Flow Example Solution
published: 25 Nov 2019
Podcast Case Study: Meet Creators Unstuck | The Flow LIVE
In this week's episode of The Flow, Katie interviews Doc & Marielou about their new podcast, Creators Unstuck. We'll learn more about the show, how they got started, and what they've learned along the way. Join us live to get your questions answered.
You can learn more about Creators Unstuck and follow the show at https://www.youtube.com/@UCXGKRDkjc_O7QH_U2dP_XlA
If you haven't already, please consider signing up for our FREE podcasting course at https://ecamm.ck.page/podcast
Welcome to The Flow. Created by Ecamm and hosted by Doc Rock and Katie Fawkes, this weekly show will take you step-by-step through the process of video podcasting. Our focus is on building efficient and effective workflows so that your content shines.
Join us Tuesdays at 12pm Eastern on YouTube and subscribe and ...
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud061/l-3523558599/m-1037198819
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud061
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud061
Georgia Tech online Master's program: https://www.udacit...
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud061
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud061
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Step by step instructions showing how to run Ford-Fulkerson on a flow network.
Code: https://github.com/msambol/dsa/blob/master/maximum_flow/ford_fulkerson.py
...
Step by step instructions showing how to run Ford-Fulkerson on a flow network.
Code: https://github.com/msambol/dsa/blob/master/maximum_flow/ford_fulkerson.py
Source: http://www.win.tue.nl/~nikhil/courses/2WO08/07NetworkFlowI.pdf
LinkedIn: https://www.linkedin.com/in/michael-sambol
Step by step instructions showing how to run Ford-Fulkerson on a flow network.
Code: https://github.com/msambol/dsa/blob/master/maximum_flow/ford_fulkerson.py
Source: http://www.win.tue.nl/~nikhil/courses/2WO08/07NetworkFlowI.pdf
LinkedIn: https://www.linkedin.com/in/michael-sambol
Explanation of how to find the maximum flow with the Ford-Fulkerson method
Next video: https://youtu.be/Xu8jjJnwvxE
Algorithms repository:
https://github.com/...
Explanation of how to find the maximum flow with the Ford-Fulkerson method
Next video: https://youtu.be/Xu8jjJnwvxE
Algorithms repository:
https://github.com/williamfiset/algorithms#network-flow
Video slides:
https://github.com/williamfiset/Algorithms/tree/master/slides
0:00 Intro and motivation for maximum flow
2:42 Basics and definitions of network flow concepts
4:26 Augmenting paths, residual edges and the residual graph
8:05 Ford-Fulkerson with DFS example
9:49 Ford-Fulkerson time complexity
11:22 Faster network flow algorithms
======================================
Practicing for interviews? I have used, and recommend `Cracking the Coding Interview` which got me a job at Google. Link on Amazon: https://amzn.to/3cvMof5
A lot of the content on this channel is inspired by the book `Competitive Programming` by Steven Halim which I frequently use as a resource and reference. Link on Amazon: https://amzn.to/3wC2nix
Support me by purchasing the full graph theory course on Udemy which includes additional problems, exercises and quizzes not available on YouTube:
https://www.udemy.com/course/graph-theory-algorithms
Explanation of how to find the maximum flow with the Ford-Fulkerson method
Next video: https://youtu.be/Xu8jjJnwvxE
Algorithms repository:
https://github.com/williamfiset/algorithms#network-flow
Video slides:
https://github.com/williamfiset/Algorithms/tree/master/slides
0:00 Intro and motivation for maximum flow
2:42 Basics and definitions of network flow concepts
4:26 Augmenting paths, residual edges and the residual graph
8:05 Ford-Fulkerson with DFS example
9:49 Ford-Fulkerson time complexity
11:22 Faster network flow algorithms
======================================
Practicing for interviews? I have used, and recommend `Cracking the Coding Interview` which got me a job at Google. Link on Amazon: https://amzn.to/3cvMof5
A lot of the content on this channel is inspired by the book `Competitive Programming` by Steven Halim which I frequently use as a resource and reference. Link on Amazon: https://amzn.to/3wC2nix
Support me by purchasing the full graph theory course on Udemy which includes additional problems, exercises and quizzes not available on YouTube:
https://www.udemy.com/course/graph-theory-algorithms
This is an alternative to the minimum cut/maximum flow theorem to find the maximum flow through a network. It seems more intuitive and less error-prone than try...
This is an alternative to the minimum cut/maximum flow theorem to find the maximum flow through a network. It seems more intuitive and less error-prone than trying to list all cuts and find the capacity of each one.
This is an alternative to the minimum cut/maximum flow theorem to find the maximum flow through a network. It seems more intuitive and less error-prone than trying to list all cuts and find the capacity of each one.
Free 5-Day Mini-Course: https://backtobackswe.com
Try Our Full Platform: https://backtobackswe.com/pricing
📹 Intuitive Video Explanations
🏃 Run Code As You ...
Free 5-Day Mini-Course: https://backtobackswe.com
Try Our Full Platform: https://backtobackswe.com/pricing
📹 Intuitive Video Explanations
🏃 Run Code As You Learn
💾 Save Progress
❓New Unseen Questions
🔎 Get All Solutions
Flow Networks: https://en.wikipedia.org/wiki/Flow_network
Ford–Fulkerson Algorithm: https://en.wikipedia.org/wiki/Ford%E2%80%93Fulkerson_algorithm
Max-Flow Min-Cut Theorem: https://en.wikipedia.org/wiki/Max-flow_min-cut_theorem
Proofs: Reference "Algorithm Design" by Jon Kleinberg and Éva Tardos Chapters 7.1, 7.2 for excellent proofs on all of this.
Things I'd Improve On This Explanation (w/ More Time):
1.) I should have done a walk-through showing how the residual graph dictates how the original graph's edge flows (f(e)) are updated each iteration. (That would've made it more clear how the residual graph in the Ford-Fulkerson algorithm tells us how to update the flow on each edge (f(e)) in the original graph along the s-t path P, THEN we update the residual graph (also along P) to prepare for the next iteration.)
2.) Go into the actual augmentation once we find an s-t path P in the residual graph. We can only modulate the flow f(e) for each edge in the original graph on path P ± the smallest value residual graph edge on path P. The smallest forward edge on path P in the residual graph is the "bottleneck" to how much we can increase flow along the path P in the original graph. (hard to visualize...the textbook may have to take it away with this one, but when you understand this you'll really get it after watching this video)
I also didn't talk about time complexity, but the amount of while loop iterations is bounded to the capacity coming out of start node 's'. We can't ever push more flow from 's' than the sum of capacities of those exiting edges. If each interaction increases the value of the flow v(f) by 1 (and v(f) starts at 0 in the beginning since no "water" is going through the "pipes"), we can do at most C augmentations of the flow network where C = sum(edge capacities leaving 's').
In each while loop:
- O(|V| + |E|) to find the augmenting path
- O(|E|) to update the flows in the original graph
- O(|E|) to update the residual graph
So total runtime can be bounded to O(C * (|V| + |E|)).
#backtobackswe #benyamephrem
Free 5-Day Mini-Course: https://backtobackswe.com
Try Our Full Platform: https://backtobackswe.com/pricing
📹 Intuitive Video Explanations
🏃 Run Code As You Learn
💾 Save Progress
❓New Unseen Questions
🔎 Get All Solutions
Flow Networks: https://en.wikipedia.org/wiki/Flow_network
Ford–Fulkerson Algorithm: https://en.wikipedia.org/wiki/Ford%E2%80%93Fulkerson_algorithm
Max-Flow Min-Cut Theorem: https://en.wikipedia.org/wiki/Max-flow_min-cut_theorem
Proofs: Reference "Algorithm Design" by Jon Kleinberg and Éva Tardos Chapters 7.1, 7.2 for excellent proofs on all of this.
Things I'd Improve On This Explanation (w/ More Time):
1.) I should have done a walk-through showing how the residual graph dictates how the original graph's edge flows (f(e)) are updated each iteration. (That would've made it more clear how the residual graph in the Ford-Fulkerson algorithm tells us how to update the flow on each edge (f(e)) in the original graph along the s-t path P, THEN we update the residual graph (also along P) to prepare for the next iteration.)
2.) Go into the actual augmentation once we find an s-t path P in the residual graph. We can only modulate the flow f(e) for each edge in the original graph on path P ± the smallest value residual graph edge on path P. The smallest forward edge on path P in the residual graph is the "bottleneck" to how much we can increase flow along the path P in the original graph. (hard to visualize...the textbook may have to take it away with this one, but when you understand this you'll really get it after watching this video)
I also didn't talk about time complexity, but the amount of while loop iterations is bounded to the capacity coming out of start node 's'. We can't ever push more flow from 's' than the sum of capacities of those exiting edges. If each interaction increases the value of the flow v(f) by 1 (and v(f) starts at 0 in the beginning since no "water" is going through the "pipes"), we can do at most C augmentations of the flow network where C = sum(edge capacities leaving 's').
In each while loop:
- O(|V| + |E|) to find the augmenting path
- O(|E|) to update the flows in the original graph
- O(|E|) to update the residual graph
So total runtime can be bounded to O(C * (|V| + |E|)).
#backtobackswe #benyamephrem
To create this video, I used a library for Manim that I have been developing for some months.
https://verdianapasqualini.github.io/GraphLibrary/
The developing ...
To create this video, I used a library for Manim that I have been developing for some months.
https://verdianapasqualini.github.io/GraphLibrary/
The developing of ManimGraphLibrary is part of my master thesis in Mathematics for Data Science @ University of Trento, Italy.
Summer of Math Exposition challenged me to produce a full video, record the voiceover and publish it instead of just working on the library.
To create this video, I used a library for Manim that I have been developing for some months.
https://verdianapasqualini.github.io/GraphLibrary/
The developing of ManimGraphLibrary is part of my master thesis in Mathematics for Data Science @ University of Trento, Italy.
Summer of Math Exposition challenged me to produce a full video, record the voiceover and publish it instead of just working on the library.
In this week's episode of The Flow, Katie interviews Doc & Marielou about their new podcast, Creators Unstuck. We'll learn more about the show, how they got sta...
In this week's episode of The Flow, Katie interviews Doc & Marielou about their new podcast, Creators Unstuck. We'll learn more about the show, how they got started, and what they've learned along the way. Join us live to get your questions answered.
You can learn more about Creators Unstuck and follow the show at https://www.youtube.com/@UCXGKRDkjc_O7QH_U2dP_XlA
If you haven't already, please consider signing up for our FREE podcasting course at https://ecamm.ck.page/podcast
Welcome to The Flow. Created by Ecamm and hosted by Doc Rock and Katie Fawkes, this weekly show will take you step-by-step through the process of video podcasting. Our focus is on building efficient and effective workflows so that your content shines.
Join us Tuesdays at 12pm Eastern on YouTube and subscribe and listen on your favorite podcast platform. Audio only podcast episodes drop a week after the live video streams.
LINKS
Episodes & show notes at https://flow.ecamm.com
Freebies at https://ecamm.tv/flowfreebies
Try Ecamm at https://www.ecamm.com
Try Descript at https://www.descript.com
Try Captivate at https://captivate.fm
Try Speedify at https://www.speedify.com
#videopodcasting #videopodcast #theflowecamm
In this week's episode of The Flow, Katie interviews Doc & Marielou about their new podcast, Creators Unstuck. We'll learn more about the show, how they got started, and what they've learned along the way. Join us live to get your questions answered.
You can learn more about Creators Unstuck and follow the show at https://www.youtube.com/@UCXGKRDkjc_O7QH_U2dP_XlA
If you haven't already, please consider signing up for our FREE podcasting course at https://ecamm.ck.page/podcast
Welcome to The Flow. Created by Ecamm and hosted by Doc Rock and Katie Fawkes, this weekly show will take you step-by-step through the process of video podcasting. Our focus is on building efficient and effective workflows so that your content shines.
Join us Tuesdays at 12pm Eastern on YouTube and subscribe and listen on your favorite podcast platform. Audio only podcast episodes drop a week after the live video streams.
LINKS
Episodes & show notes at https://flow.ecamm.com
Freebies at https://ecamm.tv/flowfreebies
Try Ecamm at https://www.ecamm.com
Try Descript at https://www.descript.com
Try Captivate at https://captivate.fm
Try Speedify at https://www.speedify.com
#videopodcasting #videopodcast #theflowecamm
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud061/l-3523558599/m-1037198819
Check out the full Advanced Operating Systems course for free at: ht...
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud061/l-3523558599/m-1037198819
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud061
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud061/l-3523558599/m-1037198819
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud061
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud061
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Step by step instructions showing how to run Ford-Fulkerson on a flow network.
Code: https://github.com/msambol/dsa/blob/master/maximum_flow/ford_fulkerson.py
Source: http://www.win.tue.nl/~nikhil/courses/2WO08/07NetworkFlowI.pdf
LinkedIn: https://www.linkedin.com/in/michael-sambol
Explanation of how to find the maximum flow with the Ford-Fulkerson method
Next video: https://youtu.be/Xu8jjJnwvxE
Algorithms repository:
https://github.com/williamfiset/algorithms#network-flow
Video slides:
https://github.com/williamfiset/Algorithms/tree/master/slides
0:00 Intro and motivation for maximum flow
2:42 Basics and definitions of network flow concepts
4:26 Augmenting paths, residual edges and the residual graph
8:05 Ford-Fulkerson with DFS example
9:49 Ford-Fulkerson time complexity
11:22 Faster network flow algorithms
======================================
Practicing for interviews? I have used, and recommend `Cracking the Coding Interview` which got me a job at Google. Link on Amazon: https://amzn.to/3cvMof5
A lot of the content on this channel is inspired by the book `Competitive Programming` by Steven Halim which I frequently use as a resource and reference. Link on Amazon: https://amzn.to/3wC2nix
Support me by purchasing the full graph theory course on Udemy which includes additional problems, exercises and quizzes not available on YouTube:
https://www.udemy.com/course/graph-theory-algorithms
This is an alternative to the minimum cut/maximum flow theorem to find the maximum flow through a network. It seems more intuitive and less error-prone than trying to list all cuts and find the capacity of each one.
Free 5-Day Mini-Course: https://backtobackswe.com
Try Our Full Platform: https://backtobackswe.com/pricing
📹 Intuitive Video Explanations
🏃 Run Code As You Learn
💾 Save Progress
❓New Unseen Questions
🔎 Get All Solutions
Flow Networks: https://en.wikipedia.org/wiki/Flow_network
Ford–Fulkerson Algorithm: https://en.wikipedia.org/wiki/Ford%E2%80%93Fulkerson_algorithm
Max-Flow Min-Cut Theorem: https://en.wikipedia.org/wiki/Max-flow_min-cut_theorem
Proofs: Reference "Algorithm Design" by Jon Kleinberg and Éva Tardos Chapters 7.1, 7.2 for excellent proofs on all of this.
Things I'd Improve On This Explanation (w/ More Time):
1.) I should have done a walk-through showing how the residual graph dictates how the original graph's edge flows (f(e)) are updated each iteration. (That would've made it more clear how the residual graph in the Ford-Fulkerson algorithm tells us how to update the flow on each edge (f(e)) in the original graph along the s-t path P, THEN we update the residual graph (also along P) to prepare for the next iteration.)
2.) Go into the actual augmentation once we find an s-t path P in the residual graph. We can only modulate the flow f(e) for each edge in the original graph on path P ± the smallest value residual graph edge on path P. The smallest forward edge on path P in the residual graph is the "bottleneck" to how much we can increase flow along the path P in the original graph. (hard to visualize...the textbook may have to take it away with this one, but when you understand this you'll really get it after watching this video)
I also didn't talk about time complexity, but the amount of while loop iterations is bounded to the capacity coming out of start node 's'. We can't ever push more flow from 's' than the sum of capacities of those exiting edges. If each interaction increases the value of the flow v(f) by 1 (and v(f) starts at 0 in the beginning since no "water" is going through the "pipes"), we can do at most C augmentations of the flow network where C = sum(edge capacities leaving 's').
In each while loop:
- O(|V| + |E|) to find the augmenting path
- O(|E|) to update the flows in the original graph
- O(|E|) to update the residual graph
So total runtime can be bounded to O(C * (|V| + |E|)).
#backtobackswe #benyamephrem
To create this video, I used a library for Manim that I have been developing for some months.
https://verdianapasqualini.github.io/GraphLibrary/
The developing of ManimGraphLibrary is part of my master thesis in Mathematics for Data Science @ University of Trento, Italy.
Summer of Math Exposition challenged me to produce a full video, record the voiceover and publish it instead of just working on the library.
In this week's episode of The Flow, Katie interviews Doc & Marielou about their new podcast, Creators Unstuck. We'll learn more about the show, how they got started, and what they've learned along the way. Join us live to get your questions answered.
You can learn more about Creators Unstuck and follow the show at https://www.youtube.com/@UCXGKRDkjc_O7QH_U2dP_XlA
If you haven't already, please consider signing up for our FREE podcasting course at https://ecamm.ck.page/podcast
Welcome to The Flow. Created by Ecamm and hosted by Doc Rock and Katie Fawkes, this weekly show will take you step-by-step through the process of video podcasting. Our focus is on building efficient and effective workflows so that your content shines.
Join us Tuesdays at 12pm Eastern on YouTube and subscribe and listen on your favorite podcast platform. Audio only podcast episodes drop a week after the live video streams.
LINKS
Episodes & show notes at https://flow.ecamm.com
Freebies at https://ecamm.tv/flowfreebies
Try Ecamm at https://www.ecamm.com
Try Descript at https://www.descript.com
Try Captivate at https://captivate.fm
Try Speedify at https://www.speedify.com
#videopodcasting #videopodcast #theflowecamm
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud061/l-3523558599/m-1037198819
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud061
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech