SlideShare uma empresa Scribd logo
1 de 22
Proving Lower Bounds to
answer the P versus NP
Question
Prerna Thakral
George Mason University
Computer Science
How did we get P versus NP?
• Turing developed a model for his
computational theory, but it failed to
account
• time
• memory
• From this – scientists divided
theoretical computer science
problems into two classes – P and
NP.
BACKGROUND
INFORMATION
What does the P class hold?
• P is for Polynomial Time.
• Consists of all those problems whose positive
solutions can be solved in an amount of time
that is polynomial to the size of the input.
What does NP class hold?
• NP stands for Nondeterministic Polynomial
Time.
• This class consists of all those problems that
can be verified in polynomial time.
Relationship between P and NP
How did P and NP come to existence
• P became the class of those problems that
were “realistically solvable.”
• NP class became important once the computer
scientists realized the large number of
problems contained in it that still needed to be
solved.
Importance and Consequences
• A proof of P equals NP could have striking
practical consequences.
• Will lead to efficient methods for solving some
important NP problems, which are fundamental to
many fields such as mathematics, biology, etc.
• A proof of P does not equal NP will have just as
great consequences.
• Will show, in a formal way, that many common
problems that can be verified easily and efficiently
cannot be solved efficiently.
CURRENT RESEARCH -
PROVING LOWER
BOUNDS
Limitations in Problem
• A limitation is seen when
computer scientists have tried to
prove lower bounds on the
complexity of problems in the
class, NP.
• Methods such as
diagonalization, the use of
pseudo-random generators and
circuits are currently being used
to prove lower bounds.
Terminology
• Diagonalization is a basic technique
used to prove that the set A does not
belong to complexity class C.
• A combinatorial circuit is a sequence
of instructions, each producing a
function based on the already
obtained previous functions.
Goal of the Research
• Develop a new technique in determining lower
bounds by conducting an experiment between
the current techniques, diagonalization, and
combinatorial circuits and comparing the results
to develop a new technique to answer the
question whether P equals NP.
EXPERIMENT
Methods and Procedures
Constants in the Experiment
• Lower bounds will be
computed on the
Travelling Salesman
Problem, an NP-
complete problem.
• The travelling salesman
problem will include 15
cities to be toured by
finding a path with the
shortest distance,
visiting each city only
once.
Trials One and Two
• Diagonalization technique - a set and function
A will be established and used to show that it
does not belong to the complexity class EXP,
which will conclude that set A is a part of the
complexity class NP.
• A circuit tree will be created from previously
defined functions. Other circuit trees will also
be created by limiting the depth of the tree and
restricting the original set and function A.
Trial Three
• Set A will use the diagonalization technique
and the combinatorial circuits simultaneously to
achieve higher efficiency than efficiency that
would have reached by using the two
techniques individually.
EXPERIMENT
Assessment
Efficiency
• Efficiency will be measured by the time required to
complete the technique and analyze the results to
see if the technique produced anything meaningful.
• Time required to find a set A, such that it does not
belong to the complexity class, EXP will be
important.
• The time required to create these various circuit
trees will also be noted, depending on whether the
depth of the tree was limited or if the original
function itself was restricted.
Success
• The experiment will be declared as successful
if the new technique which uses the two current
techniques simultaneously is seen to be more
efficient than the other techniques in proving
lower bounds.
EXPERIMENT
Next Steps
Prove P equals/does not equal NP
• By knowing how to restrict my classes, P and
NP, I will be able to determine that the
Travelling Salesman Problem is a part of the P
class.
• This will allow me to determine which other NP-
complete problems can be solved in polynomial
time, making them a part of the P class.
Publish Results
• If successful, I would like to publish my findings
in scholarly journals such as:
• IEEE Journal
• Communications of ACM IEEE
Journal

Mais conteúdo relacionado

Semelhante a Proving Lower Bounds to Answer the P versus NP question

Semelhante a Proving Lower Bounds to Answer the P versus NP question (20)

Complexity theory
Complexity theory Complexity theory
Complexity theory
 
AA ppt9107
AA ppt9107AA ppt9107
AA ppt9107
 
Unit 5
Unit 5Unit 5
Unit 5
 
Unit 5
Unit 5Unit 5
Unit 5
 
Np complete
Np completeNp complete
Np complete
 
lect5-1.ppt
lect5-1.pptlect5-1.ppt
lect5-1.ppt
 
UNIT-V.ppt
UNIT-V.pptUNIT-V.ppt
UNIT-V.ppt
 
PNP.pptx
PNP.pptxPNP.pptx
PNP.pptx
 
PNP.pptx
PNP.pptxPNP.pptx
PNP.pptx
 
NP-Completeness-myppt.pptx
NP-Completeness-myppt.pptxNP-Completeness-myppt.pptx
NP-Completeness-myppt.pptx
 
DMTM Lecture 06 Classification evaluation
DMTM Lecture 06 Classification evaluationDMTM Lecture 06 Classification evaluation
DMTM Lecture 06 Classification evaluation
 
Chapter1.1 Introduction.ppt
Chapter1.1 Introduction.pptChapter1.1 Introduction.ppt
Chapter1.1 Introduction.ppt
 
Chapter1.1 Introduction to design and analysis of algorithm.ppt
Chapter1.1 Introduction to design and analysis of algorithm.pptChapter1.1 Introduction to design and analysis of algorithm.ppt
Chapter1.1 Introduction to design and analysis of algorithm.ppt
 
DAA Mini Project.pptx
DAA Mini Project.pptxDAA Mini Project.pptx
DAA Mini Project.pptx
 
DAA Mini Project.pptx
DAA Mini Project.pptxDAA Mini Project.pptx
DAA Mini Project.pptx
 
UNIT -IV DAA.pdf
UNIT  -IV DAA.pdfUNIT  -IV DAA.pdf
UNIT -IV DAA.pdf
 
DMTM Lecture 03 Regression
DMTM Lecture 03 RegressionDMTM Lecture 03 Regression
DMTM Lecture 03 Regression
 
np complete
np completenp complete
np complete
 
Chpt7
Chpt7Chpt7
Chpt7
 
Design & implementation of machine learning algorithm in (2)
Design & implementation of machine learning algorithm in (2)Design & implementation of machine learning algorithm in (2)
Design & implementation of machine learning algorithm in (2)
 

Último

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfRankYa
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsMiki Katsuragi
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 

Último (20)

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdf
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering Tips
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 

Proving Lower Bounds to Answer the P versus NP question

  • 1. Proving Lower Bounds to answer the P versus NP Question Prerna Thakral George Mason University Computer Science
  • 2. How did we get P versus NP? • Turing developed a model for his computational theory, but it failed to account • time • memory • From this – scientists divided theoretical computer science problems into two classes – P and NP.
  • 4. What does the P class hold? • P is for Polynomial Time. • Consists of all those problems whose positive solutions can be solved in an amount of time that is polynomial to the size of the input.
  • 5. What does NP class hold? • NP stands for Nondeterministic Polynomial Time. • This class consists of all those problems that can be verified in polynomial time.
  • 7. How did P and NP come to existence • P became the class of those problems that were “realistically solvable.” • NP class became important once the computer scientists realized the large number of problems contained in it that still needed to be solved.
  • 8. Importance and Consequences • A proof of P equals NP could have striking practical consequences. • Will lead to efficient methods for solving some important NP problems, which are fundamental to many fields such as mathematics, biology, etc. • A proof of P does not equal NP will have just as great consequences. • Will show, in a formal way, that many common problems that can be verified easily and efficiently cannot be solved efficiently.
  • 10. Limitations in Problem • A limitation is seen when computer scientists have tried to prove lower bounds on the complexity of problems in the class, NP. • Methods such as diagonalization, the use of pseudo-random generators and circuits are currently being used to prove lower bounds.
  • 11. Terminology • Diagonalization is a basic technique used to prove that the set A does not belong to complexity class C. • A combinatorial circuit is a sequence of instructions, each producing a function based on the already obtained previous functions.
  • 12. Goal of the Research • Develop a new technique in determining lower bounds by conducting an experiment between the current techniques, diagonalization, and combinatorial circuits and comparing the results to develop a new technique to answer the question whether P equals NP.
  • 14. Constants in the Experiment • Lower bounds will be computed on the Travelling Salesman Problem, an NP- complete problem. • The travelling salesman problem will include 15 cities to be toured by finding a path with the shortest distance, visiting each city only once.
  • 15. Trials One and Two • Diagonalization technique - a set and function A will be established and used to show that it does not belong to the complexity class EXP, which will conclude that set A is a part of the complexity class NP. • A circuit tree will be created from previously defined functions. Other circuit trees will also be created by limiting the depth of the tree and restricting the original set and function A.
  • 16. Trial Three • Set A will use the diagonalization technique and the combinatorial circuits simultaneously to achieve higher efficiency than efficiency that would have reached by using the two techniques individually.
  • 18. Efficiency • Efficiency will be measured by the time required to complete the technique and analyze the results to see if the technique produced anything meaningful. • Time required to find a set A, such that it does not belong to the complexity class, EXP will be important. • The time required to create these various circuit trees will also be noted, depending on whether the depth of the tree was limited or if the original function itself was restricted.
  • 19. Success • The experiment will be declared as successful if the new technique which uses the two current techniques simultaneously is seen to be more efficient than the other techniques in proving lower bounds.
  • 21. Prove P equals/does not equal NP • By knowing how to restrict my classes, P and NP, I will be able to determine that the Travelling Salesman Problem is a part of the P class. • This will allow me to determine which other NP- complete problems can be solved in polynomial time, making them a part of the P class.
  • 22. Publish Results • If successful, I would like to publish my findings in scholarly journals such as: • IEEE Journal • Communications of ACM IEEE Journal