r r t r t r r st
play

r rt r tr - PowerPoint PPT Presentation

Mar 22, 2024 •209 likes •497 views

r rt r tr rst r t r ts t

  1. ❆ ▲♦❝❛❧✲❙❡❛r❝❤ ❆❧❣♦r✐t❤♠ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ▼✳ ●r♦ÿ✱ ❆✳ ●✉♣t❛✱ ❆✳ ❑✉♠❛r✱ ❏✳ ▼❛t✉s❝❤❦❡✱ ❉✳ ❙❝❤♠✐❞t✱ ❏✳ ❱❡rs❝❤❛❡ ▼✳ ❙❝❤♠✐❞t ❍✐❣❤❧✐❣❤ts ♦❢ ❆❧❣♦r✐t❤♠s ✷✵✶✼✱ ❇❡r❧✐♥ ❏✉♥❡ ✶✵t❤ ✷✵✶✼ ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✵ ✴ ✸

  2. s ❚❤❡ ❙t❡✐♥❡r ❋♦r❡st Pr♦❜❧❡♠ ■♥♣✉t ●r❛♣❤ G = ( V , E ) ❚❡r♠✐♥❛❧ ♣❛✐rs ( s ✶ , ¯ s ✶ ) , . . . , ( s k , ¯ s k ) ∈ V × V c : E → R + ❊❞❣❡ ❝♦sts ❖✉t♣✉t ▼✐♥✐♠✉♠ ❝♦st ❢♦r❡st F ⊆ E ❝♦♥t❛✐♥✐♥❣ s i ✲ ¯ s i ✲♣❛t❤ ❢♦r ❛❧❧ i = ✶ , . . . , k ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✶ ✴ ✸

  3. s ❚❤❡ ❙t❡✐♥❡r ❋♦r❡st Pr♦❜❧❡♠ ■♥♣✉t ●r❛♣❤ G = ( V , E ) ❚❡r♠✐♥❛❧ ♣❛✐rs ( s ✶ , ¯ s ✶ ) , . . . , ( s k , ¯ s k ) ∈ V × V c : E → R + ❊❞❣❡ ❝♦sts ❖✉t♣✉t ▼✐♥✐♠✉♠ ❝♦st ❢♦r❡st F ⊆ E ❝♦♥t❛✐♥✐♥❣ s i ✲ ¯ s i ✲♣❛t❤ ❢♦r ❛❧❧ i = ✶ , . . . , k ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✶ ✴ ✸

  4. s ❚❤❡ ❙t❡✐♥❡r ❋♦r❡st Pr♦❜❧❡♠ ■♥♣✉t ●r❛♣❤ G = ( V , E ) ❚❡r♠✐♥❛❧ ♣❛✐rs ( s ✶ , ¯ s ✶ ) , . . . , ( s k , ¯ s k ) ∈ V × V c : E → R + ❊❞❣❡ ❝♦sts ❖✉t♣✉t ▼✐♥✐♠✉♠ ❝♦st ❢♦r❡st F ⊆ E ❝♦♥t❛✐♥✐♥❣ s i ✲ ¯ s i ✲♣❛t❤ ❢♦r ❛❧❧ i = ✶ , . . . , k ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✶ ✴ ✸

  5. ♣❛✐rs ❚❤❡ ❙t❡✐♥❡r ❚r❡❡ Pr♦❜❧❡♠ ■♥♣✉t ●r❛♣❤ G = ( V , E ) ❚❡r♠✐♥❛❧s s ✶ , . . . , s k ∈ V c : E → R + ❊❞❣❡ ❝♦sts ❖✉t♣✉t ▼✐♥✐♠✉♠ ❝♦st tr❡❡ T ⊆ E ❝♦♥t❛✐♥✐♥❣ ❛❧❧ s i ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✶ ✴ ✸

  6. ♣❛✐rs ❚❤❡ ▼✐♥✐♠✉♠ ❙♣❛♥♥✐♥❣ ❚r❡❡ Pr♦❜❧❡♠ ■♥♣✉t ●r❛♣❤ G = ( V , E ) ❚❡r♠✐♥❛❧s V c : E → R + ❊❞❣❡ ❝♦sts ❖✉t♣✉t ▼✐♥✐♠✉♠ ❝♦st tr❡❡ T ⊆ E ❝♦♥t❛✐♥✐♥❣ ❛❧❧ v ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✶ ✴ ✸

  7. ❋♦r ▼❙❚✱ t❤✐s ✐s ♦♣t✐♠❛❧✦ ✷✲❛♣♣r♦①✐♠❛t✐♦♥ ❢♦r ❙t❡✐♥❡r ❚r❡❡ ▲♦❝❛❧ ❙❡❛r❝❤ ❊①❛♠♣❧❡✿ ▲♦❝❛❧ s❡❛r❝❤ ❢♦r ▼❙❚ ✇✐t❤ ❡✉❝❧✐❞❡❛♥ ❞✐st❛♥❝❡s ▲♦❝❛❧ s❡❛r❝❤ ❛❧❣♦r✐t❤♠✳ ✶ ❙t❛rt ❢r♦♠ ❛r❜✐tr❛r② ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥✳ ✷ ❘❡❛❝❤ ♥❡①t ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥ ❜② ❡①❡❝✉t✐♥❣ s✐♥❣❧❡ ❡❞❣❡ s✇❛♣s✳ ✸ ■t❡r❛t❡ ✉♥t✐❧ ♥♦ ✐♠♣r♦✈✐♥❣ s✇❛♣ � ▲♦❝❛❧ ♦♣t✐♠✉♠ r❡❛❝❤❡❞✳ ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✷ ✴ ✸

  8. ❋♦r ▼❙❚✱ t❤✐s ✐s ♦♣t✐♠❛❧✦ ✷✲❛♣♣r♦①✐♠❛t✐♦♥ ❢♦r ❙t❡✐♥❡r ❚r❡❡ ▲♦❝❛❧ ❙❡❛r❝❤ ❊①❛♠♣❧❡✿ ▲♦❝❛❧ s❡❛r❝❤ ❢♦r ▼❙❚ ✇✐t❤ ❡✉❝❧✐❞❡❛♥ ❞✐st❛♥❝❡s ▲♦❝❛❧ s❡❛r❝❤ ❛❧❣♦r✐t❤♠✳ ✶ ❙t❛rt ❢r♦♠ ❛r❜✐tr❛r② ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥✳ ✷ ❘❡❛❝❤ ♥❡①t ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥ ❜② ❡①❡❝✉t✐♥❣ s✐♥❣❧❡ ❡❞❣❡ s✇❛♣s✳ ✸ ■t❡r❛t❡ ✉♥t✐❧ ♥♦ ✐♠♣r♦✈✐♥❣ s✇❛♣ � ▲♦❝❛❧ ♦♣t✐♠✉♠ r❡❛❝❤❡❞✳ ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✷ ✴ ✸

  9. ❋♦r ▼❙❚✱ t❤✐s ✐s ♦♣t✐♠❛❧✦ ✷✲❛♣♣r♦①✐♠❛t✐♦♥ ❢♦r ❙t❡✐♥❡r ❚r❡❡ ▲♦❝❛❧ ❙❡❛r❝❤ ❊①❛♠♣❧❡✿ ▲♦❝❛❧ s❡❛r❝❤ ❢♦r ▼❙❚ ✇✐t❤ ❡✉❝❧✐❞❡❛♥ ❞✐st❛♥❝❡s ▲♦❝❛❧ s❡❛r❝❤ ❛❧❣♦r✐t❤♠✳ ✶ ❙t❛rt ❢r♦♠ ❛r❜✐tr❛r② ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥✳ ✷ ❘❡❛❝❤ ♥❡①t ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥ ❜② ❡①❡❝✉t✐♥❣ s✐♥❣❧❡ ❡❞❣❡ s✇❛♣s✳ ✸ ■t❡r❛t❡ ✉♥t✐❧ ♥♦ ✐♠♣r♦✈✐♥❣ s✇❛♣ � ▲♦❝❛❧ ♦♣t✐♠✉♠ r❡❛❝❤❡❞✳ ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✷ ✴ ✸

  10. ❋♦r ▼❙❚✱ t❤✐s ✐s ♦♣t✐♠❛❧✦ ✷✲❛♣♣r♦①✐♠❛t✐♦♥ ❢♦r ❙t❡✐♥❡r ❚r❡❡ ▲♦❝❛❧ ❙❡❛r❝❤ ❊①❛♠♣❧❡✿ ▲♦❝❛❧ s❡❛r❝❤ ❢♦r ▼❙❚ ✇✐t❤ ❡✉❝❧✐❞❡❛♥ ❞✐st❛♥❝❡s ▲♦❝❛❧ s❡❛r❝❤ ❛❧❣♦r✐t❤♠✳ ✶ ❙t❛rt ❢r♦♠ ❛r❜✐tr❛r② ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥✳ ✷ ❘❡❛❝❤ ♥❡①t ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥ ❜② ❡①❡❝✉t✐♥❣ s✐♥❣❧❡ ❡❞❣❡ s✇❛♣s✳ ✸ ■t❡r❛t❡ ✉♥t✐❧ ♥♦ ✐♠♣r♦✈✐♥❣ s✇❛♣ � ▲♦❝❛❧ ♦♣t✐♠✉♠ r❡❛❝❤❡❞✳ ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✷ ✴ ✸

  11. ❋♦r ▼❙❚✱ t❤✐s ✐s ♦♣t✐♠❛❧✦ ✷✲❛♣♣r♦①✐♠❛t✐♦♥ ❢♦r ❙t❡✐♥❡r ❚r❡❡ ▲♦❝❛❧ ❙❡❛r❝❤ ❊①❛♠♣❧❡✿ ▲♦❝❛❧ s❡❛r❝❤ ❢♦r ▼❙❚ ✇✐t❤ ❡✉❝❧✐❞❡❛♥ ❞✐st❛♥❝❡s ▲♦❝❛❧ s❡❛r❝❤ ❛❧❣♦r✐t❤♠✳ ✶ ❙t❛rt ❢r♦♠ ❛r❜✐tr❛r② ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥✳ ✷ ❘❡❛❝❤ ♥❡①t ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥ ❜② ❡①❡❝✉t✐♥❣ s✐♥❣❧❡ ❡❞❣❡ s✇❛♣s✳ ✸ ■t❡r❛t❡ ✉♥t✐❧ ♥♦ ✐♠♣r♦✈✐♥❣ s✇❛♣ � ▲♦❝❛❧ ♦♣t✐♠✉♠ r❡❛❝❤❡❞✳ ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✷ ✴ ✸

  12. ❋♦r ▼❙❚✱ t❤✐s ✐s ♦♣t✐♠❛❧✦ ✷✲❛♣♣r♦①✐♠❛t✐♦♥ ❢♦r ❙t❡✐♥❡r ❚r❡❡ ▲♦❝❛❧ ❙❡❛r❝❤ ❊①❛♠♣❧❡✿ ▲♦❝❛❧ s❡❛r❝❤ ❢♦r ▼❙❚ ✇✐t❤ ❡✉❝❧✐❞❡❛♥ ❞✐st❛♥❝❡s ▲♦❝❛❧ s❡❛r❝❤ ❛❧❣♦r✐t❤♠✳ ✶ ❙t❛rt ❢r♦♠ ❛r❜✐tr❛r② ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥✳ ✷ ❘❡❛❝❤ ♥❡①t ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥ ❜② ❡①❡❝✉t✐♥❣ s✐♥❣❧❡ ❡❞❣❡ s✇❛♣s✳ ✸ ■t❡r❛t❡ ✉♥t✐❧ ♥♦ ✐♠♣r♦✈✐♥❣ s✇❛♣ � ▲♦❝❛❧ ♦♣t✐♠✉♠ r❡❛❝❤❡❞✳ ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✷ ✴ ✸

  13. ❋♦r ▼❙❚✱ t❤✐s ✐s ♦♣t✐♠❛❧✦ ✷✲❛♣♣r♦①✐♠❛t✐♦♥ ❢♦r ❙t❡✐♥❡r ❚r❡❡ ▲♦❝❛❧ ❙❡❛r❝❤ ❊①❛♠♣❧❡✿ ▲♦❝❛❧ s❡❛r❝❤ ❢♦r ▼❙❚ ✇✐t❤ ❡✉❝❧✐❞❡❛♥ ❞✐st❛♥❝❡s ▲♦❝❛❧ s❡❛r❝❤ ❛❧❣♦r✐t❤♠✳ ✶ ❙t❛rt ❢r♦♠ ❛r❜✐tr❛r② ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥✳ ✷ ❘❡❛❝❤ ♥❡①t ❢❡❛s✐❜❧❡ s♦❧✉t✐♦♥ ❜② ❡①❡❝✉t✐♥❣ s✐♥❣❧❡ ❡❞❣❡ s✇❛♣s✳ ✸ ■t❡r❛t❡ ✉♥t✐❧ ♥♦ ✐♠♣r♦✈✐♥❣ s✇❛♣ � ▲♦❝❛❧ ♦♣t✐♠✉♠ r❡❛❝❤❡❞✳ ▲♦❝❛❧ ❙❡❛r❝❤ ❢♦r ❙t❡✐♥❡r ❋♦r❡st ✶✵✳✵✻✳✷✵✶✼ ✷ ✴ ✸

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Graph Algorithms Spanning Trees and Ranking Martin Mare s mares@kam.mff.cuni.cz Department

Graph Algorithms Spanning Trees and Ranking Martin Mare s mares@kam.mff.cuni.cz Department

Graph Algorithms Spanning Trees and Ranking Martin Mare s mares@kam.mff.cuni.cz Department of Applied Mathematics MFF UK Praha 2008 Martin Mare s Graph Algorithms The Minimum Spanning Tree Problem 1. Minimum Spanning Tree Problem:

550 views • 27 slides
Graph Algorithms CptS 223 Advanced Data Structures Larry Holder School of Electrical

Graph Algorithms CptS 223 Advanced Data Structures Larry Holder School of Electrical

Graph Algorithms CptS 223 Advanced Data Structures Larry Holder School of Electrical Engineering and Computer Science Washington State University 1 Minimum Spanning Trees Find a minimum-cost set of edges that connect all vertices of a

599 views • 17 slides
CS141: Intermediate Data Structures and Algorithms Graphs Amr Magdy Graph Data Structure A set

CS141: Intermediate Data Structures and Algorithms Graphs Amr Magdy Graph Data Structure A set

CS141: Intermediate Data Structures and Algorithms Graphs Amr Magdy Graph Data Structure A set of nodes (vertices) and edges connecting them 2 Graph Applications Road network Social media networks Knowledge bases 3 Graph Representations

807 views • 66 slides
Algorithms R OBERT S EDGEWICK | K EVIN W AYNE 4.3 G REEDY MST D EMO Algorithms F O U R T H E D

Algorithms R OBERT S EDGEWICK | K EVIN W AYNE 4.3 G REEDY MST D EMO Algorithms F O U R T H E D

Algorithms R OBERT S EDGEWICK | K EVIN W AYNE 4.3 G REEDY MST D EMO Algorithms F O U R T H E D I T I O N R OBERT S EDGEWICK | K EVIN W AYNE http://algs4.cs.princeton.edu Greedy MST algorithm demo Start with all edges colored gray.

1k views • 16 slides
COMP331/557 Chapter 6: Optimal Trees and Paths (Cook, Cunningham, Pulleyblank & Schrijver,

COMP331/557 Chapter 6: Optimal Trees and Paths (Cook, Cunningham, Pulleyblank & Schrijver,

COMP331/557 Chapter 6: Optimal Trees and Paths (Cook, Cunningham, Pulleyblank & Schrijver, Chapter 2) 164 Trees and Forests Definition 6.1. i An undirected graph having no circuit is called a forest. ii A connected forest is called a tree.

538 views • 26 slides
Graph Based Dependency Parsing Wei Qiu December 15, 2011 . . . . . . Graph Based

Graph Based Dependency Parsing Wei Qiu December 15, 2011 . . . . . . Graph Based

. . . . . . Graph Based Dependency Parsing Graph Based Dependency Parsing Wei Qiu December 15, 2011 . . . . . . Graph Based Dependency Parsing Outline Outline 1 Introduction to dependency parsing 2 Graph based dependency

935 views • 72 slides
MS&T Conference Materials Science and Technology (Fall Semester) What? Approx 5 day long

MS&T Conference Materials Science and Technology (Fall Semester) What? Approx 5 day long

MS&T Conference Materials Science and Technology (Fall Semester) What? Approx 5 day long conference in various cities throughout the country Attend lectures, watch/participate in competition, and network with companies and

478 views • 5 slides
Greedy Strategy [14] In the last class Undirected and Symmetric Digraph UDF Search

Greedy Strategy [14] In the last class Undirected and Symmetric Digraph UDF Search

Algorithm : Design & Analysis Greedy Strategy [14] In the last class Undirected and Symmetric Digraph UDF Search Skeleton Biconnected Components Articulation Points and Biconnectedness Biconnected Component Algorithm

536 views • 26 slides
neighbor graph Artur Czumaj DIMAP (Centre for Discrete Mathematics and it Applications)

neighbor graph Artur Czumaj DIMAP (Centre for Discrete Mathematics and it Applications)

Sublinear-time approximation of the cost of a metric k -nearest neighbor graph Artur Czumaj DIMAP (Centre for Discrete Mathematics and it Applications) Department of Computer Science University of Warwick Joint work with Christian Sohler

731 views • 42 slides
Greedy Algorithms ms Jeevani Goone*llake University of

Greedy Algorithms ms Jeevani Goone*llake University of

Greedy Algorithms ms Jeevani Goone*llake University of Colombo School of Compu*ng Sri Lanka Jeevani Goone*llake (University of Colombo - Sri

731 views • 69 slides
Minimal Spanning 9 5 Trees 14 7 8 Chapter 9 15 10 3 CPTR 318 Prims Algorithm

Minimal Spanning 9 5 Trees 14 7 8 Chapter 9 15 10 3 CPTR 318 Prims Algorithm

4/18/2013 12 6 Minimal Spanning 9 5 Trees 14 7 8 Chapter 9 15 10 3 CPTR 318 Prims Algorithm Initialization 6 12 // Initialization 6 for ( each vertex v in V ) 9 5 { 5 7 9 v.value = ; // v 14 7 v.prev = null; //

229 views • 3 slides
Objectives Minimum Spanning Tree Union-Find Data Structure Clustering Mar 1, 2019

Objectives Minimum Spanning Tree Union-Find Data Structure Clustering Mar 1, 2019

3/1/19 Objectives Minimum Spanning Tree Union-Find Data Structure Clustering Mar 1, 2019 CSCI211 - Sprenkle 1 Review What does the acronym MST stand for? What is an MST? What are some algorithms to find the MST? What

685 views • 10 slides
Reduced words in Coxeter Groups Philippe Nadeau (CNRS & Univ. Lyon 1) MADACA conference,

Reduced words in Coxeter Groups Philippe Nadeau (CNRS & Univ. Lyon 1) MADACA conference,

Reduced words in Coxeter Groups Philippe Nadeau (CNRS & Univ. Lyon 1) MADACA conference, June 21 2016 Introduction Coxeter groups are ubiquitous structures in mathematics, which can be thought of as generalized reflection groups. They are

783 views • 38 slides
Routing Topology Algorithms Mustafa Ozdal 1 Introduction How to connect nets with multiple

Routing Topology Algorithms Mustafa Ozdal 1 Introduction How to connect nets with multiple

Routing Topology Algorithms Mustafa Ozdal 1 Introduction How to connect nets with multiple terminals? Net topologies needed before point-to-point routing between terminals. Several objectives: Minimum wirelength Best timing

950 views • 48 slides
Conflict Graphs for Combinatorial Optimization Problems Ulrich Pferschy joint work with Andreas

Conflict Graphs for Combinatorial Optimization Problems Ulrich Pferschy joint work with Andreas

Introduction Knapsack Problem KCG on Trees Chordal Graph FPTAS MST Conflict Graphs for Combinatorial Optimization Problems Ulrich Pferschy joint work with Andreas Darmann and Joachim Schauer University of Graz, Austria Introduction

1.24k views • 91 slides
Minimum spanning tree Minimum spanning tree Given. Undirected graph G with positive edge weights

Minimum spanning tree Minimum spanning tree Given. Undirected graph G with positive edge weights

BBM 202 - ALGORITHMS T ODAY Minimum Spanning Trees Greedy algorithm D EPT . OF C OMPUTER E NGINEERING Edge-weighted graph API Kruskal's algorithm Prim's algorithm Context M INIMUM S PANNING T REES Apr. 5, 2016

1.02k views • 39 slides
Weighted Graphs Weighted graphs: graphs for which each edge has an Minimum Spanning Trees

Weighted Graphs Weighted graphs: graphs for which each edge has an Minimum Spanning Trees

Weighted Graphs Weighted graphs: graphs for which each edge has an Minimum Spanning Trees associated weight w(u, v), a real number value CISC4080, Computer Algorithms w: E R, weight function CIS, Fordham Univ. Graph representation

468 views • 4 slides
Distributed minimum spanning tree problem Kustaa Kangas University of Helsinki, Finland November

Distributed minimum spanning tree problem Kustaa Kangas University of Helsinki, Finland November

Distributed minimum spanning tree problem Kustaa Kangas University of Helsinki, Finland November 8, 2012 K. Kangas (U. Helsinki) Distributed minimum spanning tree problem November 8, 2012 1 / 59 Minimum spanning tree A spanning tree of an

945 views • 59 slides
Outline and Reading Minimum Spanning Trees ( 12.7.3) Minimum Spanning Tree n Definitions n A

Outline and Reading Minimum Spanning Trees ( 12.7.3) Minimum Spanning Tree n Definitions n A

Minimum Spanning Tree 7/8/03 12:53 Outline and Reading Minimum Spanning Trees ( 12.7.3) Minimum Spanning Tree n Definitions n A crucial fact Prim-Jarniks Algorithm ( 12.7.3.2) Kruskals Algorithm ( 12.7.3.1) 7/8/03 12:53 Minimum

238 views • 5 slides
Action Segmentation with Jo Join int Self-Superv rvised Temporal Domain Adaptation Min-Hung

Action Segmentation with Jo Join int Self-Superv rvised Temporal Domain Adaptation Min-Hung

ghassanalregib.info/ Action Segmentation with Jo Join int Self-Superv rvised Temporal Domain Adaptation Min-Hung Chen 1 Baopu Li 2 Yingze Bao 2 Ghassan AlRegib 1 Zsolt Kira 1 1 Georgia Institute of Technology 2 Baidu USA June 17, 2020

416 views • 13 slides
Scalable Algorithms for Distributed Statistical Inference Animashree Anandkumar School of

Scalable Algorithms for Distributed Statistical Inference Animashree Anandkumar School of

Scalable Algorithms for Distributed Statistical Inference Animashree Anandkumar School of Electrical and Computer Engineering Cornell University, Ithaca, NY 14853 Currently visiting EECS, MIT, Cambridge, MA 02139 PhD Committee: Lang Tong,

2.39k views • 162 slides
GRAIL: Scalable Reachability Index for Large Graphs Hilmi Yldrm 1 Vineet Chaoji 2 Mohammed

GRAIL: Scalable Reachability Index for Large Graphs Hilmi Yldrm 1 Vineet Chaoji 2 Mohammed

Problem Definition & Motivation Background Our Approach : GRAIL Experiments Conclusion & Future Work GRAIL: Scalable Reachability Index for Large Graphs Hilmi Yldrm 1 Vineet Chaoji 2 Mohammed J.Zaki 1 1 Rensselaer Polytechnic

992 views • 55 slides
CS481: Bioinformatics Algorithms Can Alkan EA224 calkan@cs.bilkent.edu.tr

CS481: Bioinformatics Algorithms Can Alkan EA224 calkan@cs.bilkent.edu.tr

CS481: Bioinformatics Algorithms Can Alkan EA224 calkan@cs.bilkent.edu.tr http://www.cs.bilkent.edu.tr/~calkan/teaching/cs481/ GENOME REARRANGEMENTS Turnip vs Cabbage: Different mtDNA Gene Order Gene order comparison: Similarity blocks

851 views • 64 slides
Eastern Shores (GHOTES) DNA A Family Tree DNA Project Family Tree DNA Family Tree DNA or

Eastern Shores (GHOTES) DNA A Family Tree DNA Project Family Tree DNA Family Tree DNA or

Eastern Shores (GHOTES) DNA A Family Tree DNA Project Family Tree DNA Family Tree DNA or FTDNA is one of the larger DNA testing companies. An alternative to Ancestry.com or AncestryDNA. Your (autosomal) test from another

284 views • 6 slides

More recommend