SlideShare ist ein Scribd-Unternehmen logo
1 von 22
Backtracking Technique ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Backtracking Technique ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline of Backtracking Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],1 2 3 5 7 11 4 6 8 9 10
N-Queens Problem
N-Queens Problem ,[object Object],[object Object],[object Object],Q Q Q Q 1  2  3  4  5  6  7  8 1 2 3 4 5 6 7 8
N-Queens Problem: DFS ,[object Object]
N-Queens Problem: DFS (Same Col/Row x) ,[object Object],X 1 =1 X 2 =2 3 4 4 3 4 3 4 2 3 2 3 2 2 4 3 2 3 4 1 4 1 3 2 4 1 3 4 1 2 4 3 2 1 2 3 4 (x 1 , x 2 , x 3 , x 4 )=(2, 4, 1, 3) ,[object Object],[object Object],[object Object]
N-Queens Problem: Backtracking ,[object Object],[object Object],[object Object],[object Object],Q  (i, col(i)) Q  (k, col(k)) Q (i,col(i)) Q (k,col(k))
N-Queens Problem: Backtracking ,[object Object]
N-Queens Problem ,[object Object],[object Object],[object Object],[object Object],#Nodes Checked DFS   (n n )  #Nodes Checked DFS (same col/row X) (n!) #Nodes Checked Backtracking 341 19,173,961 9.73 ⅹ10 12 1.20ⅹ 10 16 24 40,320 4.79 ⅹ10 8 8.72ⅹ 10 10 61 15,721 1.01 ⅹ10 7 3.78ⅹ 10 8 4 8 12 14 n
Graph Coloring ,[object Object],[object Object],[object Object],[object Object],V 1 V 2 V 4 V 3 2-coloring  X 3-coloring  O
Graph Coloring ,[object Object],[object Object],[object Object],start 1 2 3 2 1 3 2 1 3 2 1 3 X X X X X
Hamiltonian Circuits Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hamiltonian Circuits Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],HC : X (Eg) V 1 V 2 V 6 V 5 V 3 V 7 V 4 V 8 V 1 V 2 V 5 V 3 V 4
Sum-of-Subsets Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A w 1 w 2 w 3 w 2 w 3 w 3 w 3 O O O O O O O {w 1 ,w 2 }
Sum-of-Subsets Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],ⅹ 12 7 3 9 7 8 13 4 0 0 3 3 0 4 5 0 0 4 5 5 6 0 0 0 0 ⅹ ⅹ :  0+5+6=11<13 ⅹ ⅹ : 9+6=15>13 ⅹ ⅹ w1=3 w2=4 w3=5 w4=6 0 4 3 7
0-1 Knapsack Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],x 1 =1 x 2 =1 x 3 =1 x 4 =1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 x 2 =1
Branch-and-Bound ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Breadth First Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],BFS of a graph BFS of a tree 1 2 3 4 5 6 7 8 9 1 2 5 6 7 8 9 3 4 10 11 12 13 14 15
0-1 Knapsack Problem ,[object Object],[object Object],0 0 115 40 2 115 0 0 82 70 7 115 40 2 98 120 17 0 70 7 80 80 12 80 70 7 70 90 12 98 40 2 50 100 17 0 90 12 90 30 5 82 80 15 82 30 5 40 0 0 60 p1=40 ,  w1=2 p2=30 ,  w2=5 p3=50 ,  w3=10 p4=10 ,  w4=5
0-1 Knapsack Problem ,[object Object],[object Object],[object Object],0 0 115 40 2 115 0 0 82 70 7 115 40 2 98 120 17 0 70 7 80 80 12 80 70 7 70 90 12 98 40 2 50 100 17 0 90 12 90 30 5 82 80 15 82 30 5 40 0 0 60 p1=40 ,  w1=2 p2=30 ,  w2=5 p3=50 ,  w3=10 p4=10 ,  w4=5
0-1 Knapsack Problem ,[object Object],0 0 115 40 2 115 0 0 82 70 7 115 40 2 98 120 17 0 70 7 80 90 12 98 40 2 50 100 17 0 p1=40 ,  w1=2 p2=30 ,  w2=5 p3=50 ,  w3=10 p4=10 ,  w4=5 90 12 90

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Data Structures- Part5 recursion
Data Structures- Part5 recursionData Structures- Part5 recursion
Data Structures- Part5 recursion
 
Binary Search
Binary SearchBinary Search
Binary Search
 
Breadth First Search & Depth First Search
Breadth First Search & Depth First SearchBreadth First Search & Depth First Search
Breadth First Search & Depth First Search
 
Amortized Analysis of Algorithms
Amortized Analysis of Algorithms Amortized Analysis of Algorithms
Amortized Analysis of Algorithms
 
Daa:Dynamic Programing
Daa:Dynamic ProgramingDaa:Dynamic Programing
Daa:Dynamic Programing
 
Asymptotic notations
Asymptotic notationsAsymptotic notations
Asymptotic notations
 
Binary Search Tree
Binary Search TreeBinary Search Tree
Binary Search Tree
 
1.1 binary tree
1.1 binary tree1.1 binary tree
1.1 binary tree
 
Branch and bound
Branch and boundBranch and bound
Branch and bound
 
heap Sort Algorithm
heap  Sort Algorithmheap  Sort Algorithm
heap Sort Algorithm
 
Disjoint sets
Disjoint setsDisjoint sets
Disjoint sets
 
B and B+ tree
B and B+ treeB and B+ tree
B and B+ tree
 
Heuristic Search Techniques Unit -II.ppt
Heuristic Search Techniques Unit -II.pptHeuristic Search Techniques Unit -II.ppt
Heuristic Search Techniques Unit -II.ppt
 
5.5 back track
5.5 back track5.5 back track
5.5 back track
 
Binary Search Tree in Data Structure
Binary Search Tree in Data StructureBinary Search Tree in Data Structure
Binary Search Tree in Data Structure
 
Hashing
HashingHashing
Hashing
 
Splay Tree
Splay TreeSplay Tree
Splay Tree
 
Recursion tree method
Recursion tree methodRecursion tree method
Recursion tree method
 
Stressen's matrix multiplication
Stressen's matrix multiplicationStressen's matrix multiplication
Stressen's matrix multiplication
 
Job sequencing with deadline
Job sequencing with deadlineJob sequencing with deadline
Job sequencing with deadline
 

Andere mochten auch

Knapsack problem using dynamic programming
Knapsack problem using dynamic programmingKnapsack problem using dynamic programming
Knapsack problem using dynamic programmingkhush_boo31
 
0 1 knapsack problem using dynamic programming
0 1 knapsack problem using dynamic programming0 1 knapsack problem using dynamic programming
0 1 knapsack problem using dynamic programmingMaher Alshammari
 
Genetic Algorithm based Approach to solve Non-Fractional (0/1) Knapsack Optim...
Genetic Algorithm based Approach to solve Non-Fractional (0/1) Knapsack Optim...Genetic Algorithm based Approach to solve Non-Fractional (0/1) Knapsack Optim...
Genetic Algorithm based Approach to solve Non-Fractional (0/1) Knapsack Optim...International Islamic University
 
DESIGN AND ANALYSIS OF ALGORITHMS
DESIGN AND ANALYSIS OF ALGORITHMSDESIGN AND ANALYSIS OF ALGORITHMS
DESIGN AND ANALYSIS OF ALGORITHMSGayathri Gaayu
 

Andere mochten auch (10)

Knapsack
KnapsackKnapsack
Knapsack
 
1 blind search
1 blind search1 blind search
1 blind search
 
Knapsack problem using dynamic programming
Knapsack problem using dynamic programmingKnapsack problem using dynamic programming
Knapsack problem using dynamic programming
 
0 1 knapsack problem using dynamic programming
0 1 knapsack problem using dynamic programming0 1 knapsack problem using dynamic programming
0 1 knapsack problem using dynamic programming
 
Kruskal Algorithm
Kruskal AlgorithmKruskal Algorithm
Kruskal Algorithm
 
Genetic Algorithm based Approach to solve Non-Fractional (0/1) Knapsack Optim...
Genetic Algorithm based Approach to solve Non-Fractional (0/1) Knapsack Optim...Genetic Algorithm based Approach to solve Non-Fractional (0/1) Knapsack Optim...
Genetic Algorithm based Approach to solve Non-Fractional (0/1) Knapsack Optim...
 
DESIGN AND ANALYSIS OF ALGORITHMS
DESIGN AND ANALYSIS OF ALGORITHMSDESIGN AND ANALYSIS OF ALGORITHMS
DESIGN AND ANALYSIS OF ALGORITHMS
 
Knapsack problem
Knapsack problemKnapsack problem
Knapsack problem
 
Knapsack Problem
Knapsack ProblemKnapsack Problem
Knapsack Problem
 
Knapsack problem using fixed tuple
Knapsack problem using fixed tupleKnapsack problem using fixed tuple
Knapsack problem using fixed tuple
 

Ähnlich wie 01 knapsack using backtracking

Dynamic Programming for 4th sem cse students
Dynamic Programming for 4th sem cse studentsDynamic Programming for 4th sem cse students
Dynamic Programming for 4th sem cse studentsDeepakGowda357858
 
CS 354 Graphics Math
CS 354 Graphics MathCS 354 Graphics Math
CS 354 Graphics MathMark Kilgard
 
Recursion - Computer Algorithms
Recursion - Computer AlgorithmsRecursion - Computer Algorithms
Recursion - Computer AlgorithmsAlaa Al-Makhzoomy
 
Dynamic Programming.pptx
Dynamic Programming.pptxDynamic Programming.pptx
Dynamic Programming.pptxThanga Ramya S
 
Divide-and-Conquer & Dynamic ProgrammingDivide-and-Conqu.docx
Divide-and-Conquer & Dynamic ProgrammingDivide-and-Conqu.docxDivide-and-Conquer & Dynamic ProgrammingDivide-and-Conqu.docx
Divide-and-Conquer & Dynamic ProgrammingDivide-and-Conqu.docxjacksnathalie
 
Mathematics 9 Quadratic Functions (Module 1)
Mathematics 9 Quadratic Functions (Module 1)Mathematics 9 Quadratic Functions (Module 1)
Mathematics 9 Quadratic Functions (Module 1)Juan Miguel Palero
 
Integer_Functions .pdf
Integer_Functions .pdfInteger_Functions .pdf
Integer_Functions .pdfJainggaPotla
 
Fast parallelizable scenario-based stochastic optimization
Fast parallelizable scenario-based stochastic optimizationFast parallelizable scenario-based stochastic optimization
Fast parallelizable scenario-based stochastic optimizationPantelis Sopasakis
 
Module 1 quadratic functions
Module 1   quadratic functionsModule 1   quadratic functions
Module 1 quadratic functionsdionesioable
 
Bellman ford
Bellman fordBellman ford
Bellman fordKiran K
 
designanalysisalgorithm_unit-v-part2.pptx
designanalysisalgorithm_unit-v-part2.pptxdesignanalysisalgorithm_unit-v-part2.pptx
designanalysisalgorithm_unit-v-part2.pptxarifimad15
 
Newton Raphson method for load flow analysis
Newton Raphson method for load flow analysisNewton Raphson method for load flow analysis
Newton Raphson method for load flow analysisdivyanshuprakashrock
 
Polynomials and Curve Fitting in MATLAB
Polynomials and Curve Fitting in MATLABPolynomials and Curve Fitting in MATLAB
Polynomials and Curve Fitting in MATLABShameer Ahmed Koya
 
Litvinenko_RWTH_UQ_Seminar_talk.pdf
Litvinenko_RWTH_UQ_Seminar_talk.pdfLitvinenko_RWTH_UQ_Seminar_talk.pdf
Litvinenko_RWTH_UQ_Seminar_talk.pdfAlexander Litvinenko
 

Ähnlich wie 01 knapsack using backtracking (20)

Dynamic Programming for 4th sem cse students
Dynamic Programming for 4th sem cse studentsDynamic Programming for 4th sem cse students
Dynamic Programming for 4th sem cse students
 
2018 MUMS Fall Course - Statistical Representation of Model Input (EDITED) - ...
2018 MUMS Fall Course - Statistical Representation of Model Input (EDITED) - ...2018 MUMS Fall Course - Statistical Representation of Model Input (EDITED) - ...
2018 MUMS Fall Course - Statistical Representation of Model Input (EDITED) - ...
 
CS 354 Graphics Math
CS 354 Graphics MathCS 354 Graphics Math
CS 354 Graphics Math
 
Recursion - Computer Algorithms
Recursion - Computer AlgorithmsRecursion - Computer Algorithms
Recursion - Computer Algorithms
 
Dynamic Programming.pptx
Dynamic Programming.pptxDynamic Programming.pptx
Dynamic Programming.pptx
 
Divide-and-Conquer & Dynamic ProgrammingDivide-and-Conqu.docx
Divide-and-Conquer & Dynamic ProgrammingDivide-and-Conqu.docxDivide-and-Conquer & Dynamic ProgrammingDivide-and-Conqu.docx
Divide-and-Conquer & Dynamic ProgrammingDivide-and-Conqu.docx
 
Mathematics 9 Quadratic Functions (Module 1)
Mathematics 9 Quadratic Functions (Module 1)Mathematics 9 Quadratic Functions (Module 1)
Mathematics 9 Quadratic Functions (Module 1)
 
Backtracking
BacktrackingBacktracking
Backtracking
 
Integer_Functions .pdf
Integer_Functions .pdfInteger_Functions .pdf
Integer_Functions .pdf
 
Fast parallelizable scenario-based stochastic optimization
Fast parallelizable scenario-based stochastic optimizationFast parallelizable scenario-based stochastic optimization
Fast parallelizable scenario-based stochastic optimization
 
Module 1 quadratic functions
Module 1   quadratic functionsModule 1   quadratic functions
Module 1 quadratic functions
 
AOA ppt.ppt
AOA ppt.pptAOA ppt.ppt
AOA ppt.ppt
 
Bellman ford
Bellman fordBellman ford
Bellman ford
 
designanalysisalgorithm_unit-v-part2.pptx
designanalysisalgorithm_unit-v-part2.pptxdesignanalysisalgorithm_unit-v-part2.pptx
designanalysisalgorithm_unit-v-part2.pptx
 
Newton Raphson method for load flow analysis
Newton Raphson method for load flow analysisNewton Raphson method for load flow analysis
Newton Raphson method for load flow analysis
 
Daa chpater14
Daa chpater14Daa chpater14
Daa chpater14
 
Polynomials and Curve Fitting in MATLAB
Polynomials and Curve Fitting in MATLABPolynomials and Curve Fitting in MATLAB
Polynomials and Curve Fitting in MATLAB
 
Maths04
Maths04Maths04
Maths04
 
Chapter 04 answers
Chapter 04 answersChapter 04 answers
Chapter 04 answers
 
Litvinenko_RWTH_UQ_Seminar_talk.pdf
Litvinenko_RWTH_UQ_Seminar_talk.pdfLitvinenko_RWTH_UQ_Seminar_talk.pdf
Litvinenko_RWTH_UQ_Seminar_talk.pdf
 

Kürzlich hochgeladen

Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Patrick Viafore
 
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4jYour enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4jNeo4j
 
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideCollecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideStefan Dietze
 
How we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfHow we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfSrushith Repakula
 
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptx
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptxHarnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptx
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptxFIDO Alliance
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch TuesdayIvanti
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfFIDO Alliance
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessUXDXConf
 
Easier, Faster, and More Powerful – Notes Document Properties Reimagined
Easier, Faster, and More Powerful – Notes Document Properties ReimaginedEasier, Faster, and More Powerful – Notes Document Properties Reimagined
Easier, Faster, and More Powerful – Notes Document Properties Reimaginedpanagenda
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?Mark Billinghurst
 
Oauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftOauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftshyamraj55
 
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...FIDO Alliance
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...FIDO Alliance
 
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...FIDO Alliance
 
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfSimplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfFIDO Alliance
 
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...ScyllaDB
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxFIDO Alliance
 
Working together SRE & Platform Engineering
Working together SRE & Platform EngineeringWorking together SRE & Platform Engineering
Working together SRE & Platform EngineeringMarcus Vechiato
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingScyllaDB
 

Kürzlich hochgeladen (20)

Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4jYour enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4j
 
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideCollecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
 
How we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfHow we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdf
 
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptx
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptxHarnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptx
Harnessing Passkeys in the Battle Against AI-Powered Cyber Threats.pptx
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch Tuesday
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
 
Overview of Hyperledger Foundation
Overview of Hyperledger FoundationOverview of Hyperledger Foundation
Overview of Hyperledger Foundation
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for Success
 
Easier, Faster, and More Powerful – Notes Document Properties Reimagined
Easier, Faster, and More Powerful – Notes Document Properties ReimaginedEasier, Faster, and More Powerful – Notes Document Properties Reimagined
Easier, Faster, and More Powerful – Notes Document Properties Reimagined
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?
 
Oauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftOauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoft
 
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
 
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
 
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfSimplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
 
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptx
 
Working together SRE & Platform Engineering
Working together SRE & Platform EngineeringWorking together SRE & Platform Engineering
Working together SRE & Platform Engineering
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream Processing
 

01 knapsack using backtracking

  • 1.
  • 2.
  • 3.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.