SlideShare a Scribd company logo
DYNAMIC PROGRAMMING
Q1. Answer -     Dynamic programming is used for problems requiring a sequence of interrelated decision. This means that to take another decision we have to depend on the previous decision or solution formed.                                   dynamic programming is a recursive optimization procedure which means it’s a procedure which optimizes on a step by step basis using information from the preceding steps . We optimize as we go. In dynamic programming , a single step is sequentially related to preceding  steps and is not itself a solution to the problem.A single step contains information that identifies a segment or a part of the optimal solution e.g. time dependent problems, decision making.
Q2 Answer –  1.Stage – division of sequence of a system into various subparts is called stages 2.State – a specific measurable condition of the system 3. Recursive equation – at every stage in dynamic programming the decision rule is determined by evaluating an objective function called recursive equation. 4.Principle of optimality – it states that an optimal set of decisions rules has the property that regardless of the ith decisions, the remaining decisions must be optimal with respect to the outcome that results from the ithdecision. This means that optimal immediate decision depends only on current state and not how you got there
Q3. ANSWER-  The two basic approaches for solving dynamic programming are:- 1.)Backward recursion-  a)it is a schematic representation of a problem involving a sequence of n decisions. b)Then dynamic programming decomposes the problem into a set of n stages of analysis, each stage corresponding to one of the decisions. each stage of analysis is described by a set of elements decision, input state, output state and returns. c)Then notational representation of these element  when a backward recursion analysis is used d)Then symbolic representation of n stages of analysis using backward recursion so we can formalize the notation
The general form of the recursion equation used to compute cumulative return:-  cumulative return = direct return + cumulative return through stage             from stage            through stage i-1
2.)Forward recursion – this approach takes a problem decomposed into a sequence of n stages and analyzes the problem starting with the first stage in the sequence, working forward to the last stage it is also known as deterministic probability approach
Q4. Answer- dynamic programming is a recursive optimization procedure which means that it optimizes on a step by step basis using information from preceding steps                                                                 even in goal programming optimization occurs step by step but it was iterative rather then recursive that means that each step in goal programming represented a unique  solution that was non-optimal.in dynamic programming a single step is sequentially related to preceding steps and is not itself a solution to the problem
Q5. Answer-  Advantages - 1)`the process of breaking down a complex problem into a series of interrelated sub problems often provides insight into the nature of problem 2) Because dynamic programming is an approach to optimization rather than a technique it has flexibility that allows application to other types of mathematical programming problems 3) The computational procedure in dynamic programming allows for a built in form of sensitivity analysis based on state variables and on variables represented by stages 4)Dynamic programming achieves computational savings over complete enumeration.
Disadvantages – 1.)more expertise is required in solving dynamic programming problem then using other methods 2.)lack of general algorithm like the simplex method. It restricts computer codes necessary for inexpensive and widespread use 3.)the biggest problem is dimensionality. This problems occurs when a particular application is characterized by multiple states. It  creates lot of problem for computers capabilities & is time consuming
Dynamic Programming
Dynamic Programming
Dynamic Programming

More Related Content

What's hot

Issues in knowledge representation
Issues in knowledge representationIssues in knowledge representation
Issues in knowledge representation
Sravanthi Emani
 
I. Hill climbing algorithm II. Steepest hill climbing algorithm
I. Hill climbing algorithm II. Steepest hill climbing algorithmI. Hill climbing algorithm II. Steepest hill climbing algorithm
I. Hill climbing algorithm II. Steepest hill climbing algorithm
vikas dhakane
 
I. AO* SEARCH ALGORITHM
I. AO* SEARCH ALGORITHMI. AO* SEARCH ALGORITHM
I. AO* SEARCH ALGORITHM
vikas dhakane
 
Processor allocation in Distributed Systems
Processor allocation in Distributed SystemsProcessor allocation in Distributed Systems
Processor allocation in Distributed Systems
Ritu Ranjan Shrivastwa
 
Traveling salesman problem
Traveling salesman problemTraveling salesman problem
Traveling salesman problem
Jayesh Chauhan
 
Planning in AI(Partial order planning)
Planning in AI(Partial order planning)Planning in AI(Partial order planning)
Planning in AI(Partial order planning)
Vicky Tyagi
 
PAC Learning
PAC LearningPAC Learning
PAC Learning
Sanghyuk Chun
 
Heap Management
Heap ManagementHeap Management
Heap Management
Jenny Galino
 
Elements of dynamic programming
Elements of dynamic programmingElements of dynamic programming
Elements of dynamic programming
Tafhim Islam
 
Timestamp protocols
Timestamp protocolsTimestamp protocols
Timestamp protocols
Prashant Saini
 
Dynamic programming class 16
Dynamic programming class 16Dynamic programming class 16
Dynamic programming class 16
Kumar
 
COMPILER DESIGN Run-Time Environments
COMPILER DESIGN Run-Time EnvironmentsCOMPILER DESIGN Run-Time Environments
Minmax Algorithm In Artificial Intelligence slides
Minmax Algorithm In Artificial Intelligence slidesMinmax Algorithm In Artificial Intelligence slides
Minmax Algorithm In Artificial Intelligence slides
SamiaAziz4
 
Loop optimization
Loop optimizationLoop optimization
Loop optimization
Vivek Gandhi
 
program partitioning and scheduling IN Advanced Computer Architecture
program partitioning and scheduling  IN Advanced Computer Architectureprogram partitioning and scheduling  IN Advanced Computer Architecture
program partitioning and scheduling IN Advanced Computer Architecture
Pankaj Kumar Jain
 
Peephole Optimization
Peephole OptimizationPeephole Optimization
Peephole Optimization
United International University
 
COCOMO Model in software project management
COCOMO Model in software project managementCOCOMO Model in software project management
COCOMO Model in software project management
Syed Hassan Ali
 
Error Detection & Recovery
Error Detection & RecoveryError Detection & Recovery
Error Detection & Recovery
Akhil Kaushik
 
search strategies in artificial intelligence
search strategies in artificial intelligencesearch strategies in artificial intelligence
search strategies in artificial intelligence
Hanif Ullah (Gold Medalist)
 
Introduction to dynamic programming
Introduction to dynamic programmingIntroduction to dynamic programming
Introduction to dynamic programming
Amisha Narsingani
 

What's hot (20)

Issues in knowledge representation
Issues in knowledge representationIssues in knowledge representation
Issues in knowledge representation
 
I. Hill climbing algorithm II. Steepest hill climbing algorithm
I. Hill climbing algorithm II. Steepest hill climbing algorithmI. Hill climbing algorithm II. Steepest hill climbing algorithm
I. Hill climbing algorithm II. Steepest hill climbing algorithm
 
I. AO* SEARCH ALGORITHM
I. AO* SEARCH ALGORITHMI. AO* SEARCH ALGORITHM
I. AO* SEARCH ALGORITHM
 
Processor allocation in Distributed Systems
Processor allocation in Distributed SystemsProcessor allocation in Distributed Systems
Processor allocation in Distributed Systems
 
Traveling salesman problem
Traveling salesman problemTraveling salesman problem
Traveling salesman problem
 
Planning in AI(Partial order planning)
Planning in AI(Partial order planning)Planning in AI(Partial order planning)
Planning in AI(Partial order planning)
 
PAC Learning
PAC LearningPAC Learning
PAC Learning
 
Heap Management
Heap ManagementHeap Management
Heap Management
 
Elements of dynamic programming
Elements of dynamic programmingElements of dynamic programming
Elements of dynamic programming
 
Timestamp protocols
Timestamp protocolsTimestamp protocols
Timestamp protocols
 
Dynamic programming class 16
Dynamic programming class 16Dynamic programming class 16
Dynamic programming class 16
 
COMPILER DESIGN Run-Time Environments
COMPILER DESIGN Run-Time EnvironmentsCOMPILER DESIGN Run-Time Environments
COMPILER DESIGN Run-Time Environments
 
Minmax Algorithm In Artificial Intelligence slides
Minmax Algorithm In Artificial Intelligence slidesMinmax Algorithm In Artificial Intelligence slides
Minmax Algorithm In Artificial Intelligence slides
 
Loop optimization
Loop optimizationLoop optimization
Loop optimization
 
program partitioning and scheduling IN Advanced Computer Architecture
program partitioning and scheduling  IN Advanced Computer Architectureprogram partitioning and scheduling  IN Advanced Computer Architecture
program partitioning and scheduling IN Advanced Computer Architecture
 
Peephole Optimization
Peephole OptimizationPeephole Optimization
Peephole Optimization
 
COCOMO Model in software project management
COCOMO Model in software project managementCOCOMO Model in software project management
COCOMO Model in software project management
 
Error Detection & Recovery
Error Detection & RecoveryError Detection & Recovery
Error Detection & Recovery
 
search strategies in artificial intelligence
search strategies in artificial intelligencesearch strategies in artificial intelligence
search strategies in artificial intelligence
 
Introduction to dynamic programming
Introduction to dynamic programmingIntroduction to dynamic programming
Introduction to dynamic programming
 

Similar to Dynamic Programming

Building blocks of Algblocks of Alg.pptx
Building blocks of Algblocks of Alg.pptxBuilding blocks of Algblocks of Alg.pptx
Building blocks of Algblocks of Alg.pptx
NISHASOMSCS113
 
PROBLEM SOLVING TECHNIQUES
PROBLEM SOLVING TECHNIQUESPROBLEM SOLVING TECHNIQUES
PROBLEM SOLVING TECHNIQUES
sudhanagarajan5
 
Csc 102 lecture note(introduction to problem solving)
Csc 102 lecture note(introduction to problem solving)Csc 102 lecture note(introduction to problem solving)
Csc 102 lecture note(introduction to problem solving)
Christopher Chizoba
 
Unit 1 python (2021 r)
Unit 1 python (2021 r)Unit 1 python (2021 r)
Unit 1 python (2021 r)
praveena p
 
Dynamic programming prasintation eaisy
Dynamic programming prasintation eaisyDynamic programming prasintation eaisy
Dynamic programming prasintation eaisy
ahmed51236
 
A brief study on linear programming solving methods
A brief study on linear programming solving methodsA brief study on linear programming solving methods
A brief study on linear programming solving methods
MayurjyotiNeog
 
2-Algorithms and Complexit data structurey.pdf
2-Algorithms and Complexit data structurey.pdf2-Algorithms and Complexit data structurey.pdf
2-Algorithms and Complexit data structurey.pdf
ishan743441
 
Dynamic programming 2
Dynamic programming 2Dynamic programming 2
Dynamic programming 2
Roy Thomas
 
Operation Research Techniques
Operation Research Techniques Operation Research Techniques
Operation Research Techniques
Lijin Mathew
 
C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHES
C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHESC LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHES
C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHES
HarshJha34
 
CH-1.1 Introduction (1).pptx
CH-1.1 Introduction (1).pptxCH-1.1 Introduction (1).pptx
CH-1.1 Introduction (1).pptx
satvikkushwaha1
 
Unit.2. linear programming
Unit.2. linear programmingUnit.2. linear programming
Unit.2. linear programming
DagnaygebawGoshme
 
Module 2ppt.pptx divid and conquer method
Module 2ppt.pptx divid and conquer methodModule 2ppt.pptx divid and conquer method
Module 2ppt.pptx divid and conquer method
JyoReddy9
 
Paper review: Learned Optimizers that Scale and Generalize.
Paper review: Learned Optimizers that Scale and Generalize.Paper review: Learned Optimizers that Scale and Generalize.
Paper review: Learned Optimizers that Scale and Generalize.
Wuhyun Rico Shin
 
linear programming
linear programming linear programming
linear programming
DagnaygebawGoshme
 
Glenn Vanderburg — Real software engineering
Glenn Vanderburg — Real software engineeringGlenn Vanderburg — Real software engineering
Glenn Vanderburg — Real software engineering
atr2006
 
Real software engineering
Real software engineeringReal software engineering
Real software engineering
atr2006
 
Operating system 23 process synchronization
Operating system 23 process synchronizationOperating system 23 process synchronization
Operating system 23 process synchronization
Vaibhav Khanna
 
Design & Analysis of Algorithm course .pptx
Design & Analysis of Algorithm course .pptxDesign & Analysis of Algorithm course .pptx
Design & Analysis of Algorithm course .pptx
JeevaMCSEKIOT
 
An efficient use of temporal difference technique in Computer Game Learning
An efficient use of temporal difference technique in Computer Game LearningAn efficient use of temporal difference technique in Computer Game Learning
An efficient use of temporal difference technique in Computer Game Learning
Prabhu Kumar
 

Similar to Dynamic Programming (20)

Building blocks of Algblocks of Alg.pptx
Building blocks of Algblocks of Alg.pptxBuilding blocks of Algblocks of Alg.pptx
Building blocks of Algblocks of Alg.pptx
 
PROBLEM SOLVING TECHNIQUES
PROBLEM SOLVING TECHNIQUESPROBLEM SOLVING TECHNIQUES
PROBLEM SOLVING TECHNIQUES
 
Csc 102 lecture note(introduction to problem solving)
Csc 102 lecture note(introduction to problem solving)Csc 102 lecture note(introduction to problem solving)
Csc 102 lecture note(introduction to problem solving)
 
Unit 1 python (2021 r)
Unit 1 python (2021 r)Unit 1 python (2021 r)
Unit 1 python (2021 r)
 
Dynamic programming prasintation eaisy
Dynamic programming prasintation eaisyDynamic programming prasintation eaisy
Dynamic programming prasintation eaisy
 
A brief study on linear programming solving methods
A brief study on linear programming solving methodsA brief study on linear programming solving methods
A brief study on linear programming solving methods
 
2-Algorithms and Complexit data structurey.pdf
2-Algorithms and Complexit data structurey.pdf2-Algorithms and Complexit data structurey.pdf
2-Algorithms and Complexit data structurey.pdf
 
Dynamic programming 2
Dynamic programming 2Dynamic programming 2
Dynamic programming 2
 
Operation Research Techniques
Operation Research Techniques Operation Research Techniques
Operation Research Techniques
 
C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHES
C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHESC LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHES
C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHES
 
CH-1.1 Introduction (1).pptx
CH-1.1 Introduction (1).pptxCH-1.1 Introduction (1).pptx
CH-1.1 Introduction (1).pptx
 
Unit.2. linear programming
Unit.2. linear programmingUnit.2. linear programming
Unit.2. linear programming
 
Module 2ppt.pptx divid and conquer method
Module 2ppt.pptx divid and conquer methodModule 2ppt.pptx divid and conquer method
Module 2ppt.pptx divid and conquer method
 
Paper review: Learned Optimizers that Scale and Generalize.
Paper review: Learned Optimizers that Scale and Generalize.Paper review: Learned Optimizers that Scale and Generalize.
Paper review: Learned Optimizers that Scale and Generalize.
 
linear programming
linear programming linear programming
linear programming
 
Glenn Vanderburg — Real software engineering
Glenn Vanderburg — Real software engineeringGlenn Vanderburg — Real software engineering
Glenn Vanderburg — Real software engineering
 
Real software engineering
Real software engineeringReal software engineering
Real software engineering
 
Operating system 23 process synchronization
Operating system 23 process synchronizationOperating system 23 process synchronization
Operating system 23 process synchronization
 
Design & Analysis of Algorithm course .pptx
Design & Analysis of Algorithm course .pptxDesign & Analysis of Algorithm course .pptx
Design & Analysis of Algorithm course .pptx
 
An efficient use of temporal difference technique in Computer Game Learning
An efficient use of temporal difference technique in Computer Game LearningAn efficient use of temporal difference technique in Computer Game Learning
An efficient use of temporal difference technique in Computer Game Learning
 

More from paramalways

Competition Act, 2002
Competition Act, 2002Competition Act, 2002
Competition Act, 2002
paramalways
 
Environment Act, 1986
Environment Act, 1986Environment Act, 1986
Environment Act, 1986paramalways
 
Statistics All
Statistics AllStatistics All
Statistics All
paramalways
 
software engineering
software engineeringsoftware engineering
software engineering
paramalways
 
Iti Lprocessmgmt
Iti LprocessmgmtIti Lprocessmgmt
Iti Lprocessmgmt
paramalways
 
It Service Management
It Service ManagementIt Service Management
It Service Management
paramalways
 
Security And Ethical Challenges Of Infornation Technology
Security And Ethical Challenges Of Infornation TechnologySecurity And Ethical Challenges Of Infornation Technology
Security And Ethical Challenges Of Infornation Technology
paramalways
 
Dm Ps Analysis
Dm Ps AnalysisDm Ps Analysis
Dm Ps Analysis
paramalways
 
Decision Support System
Decision Support SystemDecision Support System
Decision Support System
paramalways
 
Bis Data Information
Bis Data InformationBis Data Information
Bis Data Information
paramalways
 
Bis Tools Of It
Bis Tools Of ItBis Tools Of It
Bis Tools Of It
paramalways
 
Basics Of Networking
Basics Of NetworkingBasics Of Networking
Basics Of Networking
paramalways
 

More from paramalways (12)

Competition Act, 2002
Competition Act, 2002Competition Act, 2002
Competition Act, 2002
 
Environment Act, 1986
Environment Act, 1986Environment Act, 1986
Environment Act, 1986
 
Statistics All
Statistics AllStatistics All
Statistics All
 
software engineering
software engineeringsoftware engineering
software engineering
 
Iti Lprocessmgmt
Iti LprocessmgmtIti Lprocessmgmt
Iti Lprocessmgmt
 
It Service Management
It Service ManagementIt Service Management
It Service Management
 
Security And Ethical Challenges Of Infornation Technology
Security And Ethical Challenges Of Infornation TechnologySecurity And Ethical Challenges Of Infornation Technology
Security And Ethical Challenges Of Infornation Technology
 
Dm Ps Analysis
Dm Ps AnalysisDm Ps Analysis
Dm Ps Analysis
 
Decision Support System
Decision Support SystemDecision Support System
Decision Support System
 
Bis Data Information
Bis Data InformationBis Data Information
Bis Data Information
 
Bis Tools Of It
Bis Tools Of ItBis Tools Of It
Bis Tools Of It
 
Basics Of Networking
Basics Of NetworkingBasics Of Networking
Basics Of Networking
 

Recently uploaded

Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
c5vrf27qcz
 
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptxThe Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
operationspcvita
 
Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!
Ortus Solutions, Corp
 
Discover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched ContentDiscover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched Content
ScyllaDB
 
GNSS spoofing via SDR (Criptored Talks 2024)
GNSS spoofing via SDR (Criptored Talks 2024)GNSS spoofing via SDR (Criptored Talks 2024)
GNSS spoofing via SDR (Criptored Talks 2024)
Javier Junquera
 
Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips
Getting the Most Out of ScyllaDB Monitoring: ShareChat's TipsGetting the Most Out of ScyllaDB Monitoring: ShareChat's Tips
Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips
ScyllaDB
 
"What does it really mean for your system to be available, or how to define w...
"What does it really mean for your system to be available, or how to define w..."What does it really mean for your system to be available, or how to define w...
"What does it really mean for your system to be available, or how to define w...
Fwdays
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
DianaGray10
 
Containers & AI - Beauty and the Beast!?!
Containers & AI - Beauty and the Beast!?!Containers & AI - Beauty and the Beast!?!
Containers & AI - Beauty and the Beast!?!
Tobias Schneck
 
Demystifying Knowledge Management through Storytelling
Demystifying Knowledge Management through StorytellingDemystifying Knowledge Management through Storytelling
Demystifying Knowledge Management through Storytelling
Enterprise Knowledge
 
"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota
Fwdays
 
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and BioinformaticiansBiomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Neo4j
 
Christine's Supplier Sourcing Presentaion.pptx
Christine's Supplier Sourcing Presentaion.pptxChristine's Supplier Sourcing Presentaion.pptx
Christine's Supplier Sourcing Presentaion.pptx
christinelarrosa
 
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...
"$10 thousand per minute of downtime: architecture, queues, streaming and fin..."$10 thousand per minute of downtime: architecture, queues, streaming and fin...
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...
Fwdays
 
Session 1 - Intro to Robotic Process Automation.pdf
Session 1 - Intro to Robotic Process Automation.pdfSession 1 - Intro to Robotic Process Automation.pdf
Session 1 - Intro to Robotic Process Automation.pdf
UiPathCommunity
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
Miro Wengner
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
Jason Yip
 
Principle of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptxPrinciple of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptx
BibashShahi
 
"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba
"NATO Hackathon Winner: AI-Powered Drug Search",  Taras Kloba"NATO Hackathon Winner: AI-Powered Drug Search",  Taras Kloba
"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba
Fwdays
 
What is an RPA CoE? Session 2 – CoE Roles
What is an RPA CoE?  Session 2 – CoE RolesWhat is an RPA CoE?  Session 2 – CoE Roles
What is an RPA CoE? Session 2 – CoE Roles
DianaGray10
 

Recently uploaded (20)

Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
 
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptxThe Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
 
Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!
 
Discover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched ContentDiscover the Unseen: Tailored Recommendation of Unwatched Content
Discover the Unseen: Tailored Recommendation of Unwatched Content
 
GNSS spoofing via SDR (Criptored Talks 2024)
GNSS spoofing via SDR (Criptored Talks 2024)GNSS spoofing via SDR (Criptored Talks 2024)
GNSS spoofing via SDR (Criptored Talks 2024)
 
Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips
Getting the Most Out of ScyllaDB Monitoring: ShareChat's TipsGetting the Most Out of ScyllaDB Monitoring: ShareChat's Tips
Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips
 
"What does it really mean for your system to be available, or how to define w...
"What does it really mean for your system to be available, or how to define w..."What does it really mean for your system to be available, or how to define w...
"What does it really mean for your system to be available, or how to define w...
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
 
Containers & AI - Beauty and the Beast!?!
Containers & AI - Beauty and the Beast!?!Containers & AI - Beauty and the Beast!?!
Containers & AI - Beauty and the Beast!?!
 
Demystifying Knowledge Management through Storytelling
Demystifying Knowledge Management through StorytellingDemystifying Knowledge Management through Storytelling
Demystifying Knowledge Management through Storytelling
 
"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota
 
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and BioinformaticiansBiomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
 
Christine's Supplier Sourcing Presentaion.pptx
Christine's Supplier Sourcing Presentaion.pptxChristine's Supplier Sourcing Presentaion.pptx
Christine's Supplier Sourcing Presentaion.pptx
 
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...
"$10 thousand per minute of downtime: architecture, queues, streaming and fin..."$10 thousand per minute of downtime: architecture, queues, streaming and fin...
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...
 
Session 1 - Intro to Robotic Process Automation.pdf
Session 1 - Intro to Robotic Process Automation.pdfSession 1 - Intro to Robotic Process Automation.pdf
Session 1 - Intro to Robotic Process Automation.pdf
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
 
Principle of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptxPrinciple of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptx
 
"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba
"NATO Hackathon Winner: AI-Powered Drug Search",  Taras Kloba"NATO Hackathon Winner: AI-Powered Drug Search",  Taras Kloba
"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba
 
What is an RPA CoE? Session 2 – CoE Roles
What is an RPA CoE?  Session 2 – CoE RolesWhat is an RPA CoE?  Session 2 – CoE Roles
What is an RPA CoE? Session 2 – CoE Roles
 

Dynamic Programming

  • 2. Q1. Answer - Dynamic programming is used for problems requiring a sequence of interrelated decision. This means that to take another decision we have to depend on the previous decision or solution formed. dynamic programming is a recursive optimization procedure which means it’s a procedure which optimizes on a step by step basis using information from the preceding steps . We optimize as we go. In dynamic programming , a single step is sequentially related to preceding steps and is not itself a solution to the problem.A single step contains information that identifies a segment or a part of the optimal solution e.g. time dependent problems, decision making.
  • 3. Q2 Answer – 1.Stage – division of sequence of a system into various subparts is called stages 2.State – a specific measurable condition of the system 3. Recursive equation – at every stage in dynamic programming the decision rule is determined by evaluating an objective function called recursive equation. 4.Principle of optimality – it states that an optimal set of decisions rules has the property that regardless of the ith decisions, the remaining decisions must be optimal with respect to the outcome that results from the ithdecision. This means that optimal immediate decision depends only on current state and not how you got there
  • 4. Q3. ANSWER- The two basic approaches for solving dynamic programming are:- 1.)Backward recursion- a)it is a schematic representation of a problem involving a sequence of n decisions. b)Then dynamic programming decomposes the problem into a set of n stages of analysis, each stage corresponding to one of the decisions. each stage of analysis is described by a set of elements decision, input state, output state and returns. c)Then notational representation of these element when a backward recursion analysis is used d)Then symbolic representation of n stages of analysis using backward recursion so we can formalize the notation
  • 5. The general form of the recursion equation used to compute cumulative return:- cumulative return = direct return + cumulative return through stage from stage through stage i-1
  • 6. 2.)Forward recursion – this approach takes a problem decomposed into a sequence of n stages and analyzes the problem starting with the first stage in the sequence, working forward to the last stage it is also known as deterministic probability approach
  • 7. Q4. Answer- dynamic programming is a recursive optimization procedure which means that it optimizes on a step by step basis using information from preceding steps even in goal programming optimization occurs step by step but it was iterative rather then recursive that means that each step in goal programming represented a unique solution that was non-optimal.in dynamic programming a single step is sequentially related to preceding steps and is not itself a solution to the problem
  • 8. Q5. Answer- Advantages - 1)`the process of breaking down a complex problem into a series of interrelated sub problems often provides insight into the nature of problem 2) Because dynamic programming is an approach to optimization rather than a technique it has flexibility that allows application to other types of mathematical programming problems 3) The computational procedure in dynamic programming allows for a built in form of sensitivity analysis based on state variables and on variables represented by stages 4)Dynamic programming achieves computational savings over complete enumeration.
  • 9. Disadvantages – 1.)more expertise is required in solving dynamic programming problem then using other methods 2.)lack of general algorithm like the simplex method. It restricts computer codes necessary for inexpensive and widespread use 3.)the biggest problem is dimensionality. This problems occurs when a particular application is characterized by multiple states. It creates lot of problem for computers capabilities & is time consuming