Dynamic Programming Algorithm CSI-504.pdf
0 likes27 views
Dynamic programming (DP) is a technique used to solve complex problems by breaking them into simpler subproblems, solving each only once, and storing their results to enhance efficiency. It can be applied through two main approaches: top-down (memoization) and bottom-up (tabulation), both allowing for optimal solutions without redundant computations. DP is utilized in various fields including optimization, computer science, and operations research.
1 of 11
Download to read offline
Ad
Recommended
Dynamic Programing.pptx good for understanding
Dynamic Programing.pptx good for understandingHUSNAINAHMAD39 Dynamic programming (DP) is a method for solving complex problems by breaking them into simpler, overlapping subproblems, and storing their results to avoid redundancy. Key principles include overlapping subproblems and optimal substructure, illustrated through examples like the Fibonacci sequence. DP can be approached using two methods: top-down (memoization) and bottom-up (tabulation), each facilitating efficient problem-solving.
Dynamic Programming: Optimizing Solutions
Dynamic Programming: Optimizing Solutionsiiillusion23 Dynamic programming (DP) is an optimization technique for solving problems with overlapping subproblems and optimal substructure by breaking them down into smaller parts and reusing solutions. It applies to various data structures, including arrays, graphs, and strings, and can effectively tackle complex problems like knapsack and traveling salesman. Despite its advantages in efficiency and organization, DP faces challenges like high memory consumption and difficulty in identifying suitable problems.
Algorithm Design Technique
Algorithm Design Technique Bharat Bhushan The document presents an overview of dynamic programming as an algorithm design technique, emphasizing its application in solving optimization problems through the breakdown into simpler sub-problems. It outlines both top-down and bottom-up approaches, detailing their methodology and differences, along with examples of problems solvable by dynamic programming. Additionally, the document discusses the advantages and disadvantages of the dynamic programming approach, highlighting aspects such as coding efficiency and memory considerations.
Dynamic Programming
Dynamic ProgrammingBharat Bhushan The document presents an overview of dynamic programming in algorithm design, highlighting its method of breaking down complex problems into simpler sub-problems and storing their optimal solutions through memorization. It discusses two approaches—top-down and bottom-up—along with examples of problems suited for dynamic programming, such as the Fibonacci series and the knapsack problem. The advantages of dynamic programming include obtaining optimal solutions and ease of debugging, while its disadvantages involve higher memory usage due to recursion and potential stack overflow issues.
Module 2ppt.pptx divid and conquer method
Module 2ppt.pptx divid and conquer methodJyoReddy9 This document discusses dynamic programming and provides examples of problems that can be solved using dynamic programming. It covers the following key points:
- Dynamic programming can be used to solve problems that exhibit optimal substructure and overlapping subproblems. It works by breaking problems down into subproblems and storing the results of subproblems to avoid recomputing them.
- Examples of problems discussed include matrix chain multiplication, all pairs shortest path, optimal binary search trees, 0/1 knapsack problem, traveling salesperson problem, and flow shop scheduling.
- The document provides pseudocode for algorithms to solve matrix chain multiplication and optimal binary search trees using dynamic programming. It also explains the basic steps and principles of dynamic programming algorithm design
Dynamic programming 2
Dynamic programming 2Roy Thomas Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. It is applicable when problems exhibit overlapping subproblems that are only slightly smaller. The method involves 4 steps: 1) developing a mathematical notation to express solutions, 2) proving the principle of optimality holds, 3) deriving a recurrence relation relating solutions to subsolutions, and 4) writing an algorithm to compute the recurrence relation. Dynamic programming yields optimal solutions when the principle of optimality holds, without needing to prove optimality. It is used to solve production, scheduling, resource allocation, and inventory problems.
DYNAMIC_________________________PROGRAMMING.pptx
DYNAMIC_________________________PROGRAMMING.pptxnazmusshakib335 Dynamic programming is a method for solving complex problems by breaking them down into simpler overlapping subproblems with optimal substructure. It is often preferred over greedy methods for optimization problems due to its efficient use of previously solved subproblems through approaches like memoization (top-down) and tabulation (bottom-up). While it can reduce time complexity and avoid redundant calculations, dynamic programming may require significant memory and necessitates the identification of suitable subproblems.
Annotaed slides for dynamic programming algorithm
Annotaed slides for dynamic programming algorithmjohnathangamal27 The document discusses the analysis and design of algorithms, focusing on dynamic programming as a strategy for solving optimization problems efficiently. It outlines key concepts, such as optimal substructure and overlapping subproblems, and provides examples like the Fibonacci sequence and longest common subsequence. The material also highlights algorithm design techniques, comparisons between recursive and dynamic programming solutions, and factors influencing time-space trade-offs.
Dynamic programming
Dynamic programmingYıldırım Tam A. Plannig
The document mentions that in dynamic programming, "Programming" refers to "planning", not computer programming.
Dynamic programming
Dynamic programmingMd.Mojibul Hoque Dynamic programming is an algorithm design technique for optimization problems that involves breaking down problems into sub-problems and storing solutions to sub-problems in tables to avoid recomputing them. It is used to minimize or maximize problems by combining solutions to sub-problems, with sub-problems potentially sharing sub-sub-problems. Dynamic programming reduces time complexity by increasing space complexity through storing optimal solutions to sub-problems in tables.
Dynamic programming
Dynamic programmingJay Nagar Dynamic programming is a method for solving complex problems by breaking them into simpler subproblems, solving each only once, and storing their solutions. It is applicable in optimization problems where optimal substructure and overlapping subproblems are present, allowing for efficient solutions compared to divide-and-conquer strategies. The process involves characterizing the optimal solution structure, defining the problem recursively, computing values systematically, and sometimes constructing the optimal solution based on these computed values.
Presentation (1) (5).pptx in DAA computer
Presentation (1) (5).pptx in DAA computerMadhurimaKomanduru The document presents an overview of dynamic programming, outlining its definition, method of operation, and applications in solving complex problems efficiently. Key applications discussed include the shortest path problem, knapsack problem, and the Floyd-Warshall algorithm, which is specifically highlighted for its simplicity and effectiveness in finding shortest paths in weighted graphs. The advantages of dynamic programming include its efficiency, ability to provide optimal solutions, and suitability for complex problem-solving.
Presentation (1) (5).pptx in DAA computer
Presentation (1) (5).pptx in DAA computerMadhurimaKomanduru The document discusses dynamic programming, an algorithmic technique for solving complex problems by dividing them into smaller overlapping subproblems. It covers applications such as the shortest path problem, knapsack problem, and the Travelling Salesman problem, also detailing the Floyd-Warshall algorithm for finding all pair shortest paths in a weighted graph. Advantages of dynamic programming include efficiency, optimal solutions, and memory efficiency.
Introduction to Dynamic Programming, Principle of Optimality
Introduction to Dynamic Programming, Principle of OptimalityBhavin Darji Dynamic programming is an optimization technique invented by Richard Bellman in 1950, designed to solve problems with overlapping subproblems by storing solutions to avoid recomputation. It involves characterizing the structure of optimal solutions, defining those solutions recursively, and computing the solutions either through top-down caching or a bottom-up approach. Applications of dynamic programming include problems like the knapsack problem, shortest path calculations, and matrix chain multiplication.
W8L1 Introduction & Fibonacci Numbers part 1.pptx
W8L1 Introduction & Fibonacci Numbers part 1.pptxsakibahmed181234 The document provides an introduction to dynamic programming, particularly focusing on solving the Fibonacci numbers problem. It discusses key components of dynamic programming, including optimal substructures, overlapping subproblems, and the importance of tabular computation to avoid redundant calculations. The algorithm's structure involves characterizing optimal solutions, defining their values recursively, and constructing solutions in a bottom-up manner.
Dynamic programming in Design Analysis and Algorithms
Dynamic programming in Design Analysis and AlgorithmsNikunjGoyal20 Dynamic programming is an optimization approach that breaks down problems into overlapping sub-problems whose results are reused (memoization). It contrasts with divide and conquer by relying on previous outputs to optimize larger sub-problems, with two main approaches: top-down (recursive with memoization) and bottom-up (iterative with tabulation). Key applications include the Fibonacci series, knapsack problem, and Bellman-Ford algorithm for shortest paths.
Dynamic Programming
Dynamic Programmingparamalways Dynamic programming is a recursive optimization technique used to solve problems with interrelated decisions. It breaks the problem down into sequential steps, where each step builds on the solutions to previous steps. The optimal solution is determined by working through each step in order. Dynamic programming has advantages like computational savings over complete enumeration and providing insight into problem nature. However, it also has disadvantages like requiring more expertise, lacking general algorithms, and facing dimensionality problems for applications with multiple states.
Dynamic Programming.pptx
Dynamic Programming.pptxMuktarHossain13 Dynamic programming is a technique that breaks problems into subproblems and saves results to optimize solutions without recomputing subproblems. It is commonly used in computer science, mathematics, economics, and operations research for problems like the Fibonacci series, knapsack problem, and traveling salesman problem. Dynamic programming improves efficiency by storing subproblem solutions and avoiding redundant calculations. It can find optimal and approximate solutions to large problems. For the Fibonacci series, a dynamic programming approach builds up the sequence by calculating each term from the previous two terms rather than recursively calculating all subproblems.
Pintu ram
Pintu rampinturam2 Dynamic programming is an algorithm design technique for solving complex problems defined by overlapping subproblems. It works by solving each subproblem only once and storing the results for future use, avoiding the work of recomputing results. The principle of optimality states that optimal solutions can be constructed from optimal solutions of subproblems. Dynamic programming algorithms involve characterizing the optimal substructure, recursively defining the optimal value, computing the optimal value in a bottom-up manner, and constructing an optimal solution. It is generally applied to optimization problems and provides efficient solutions, though it may not always find a global optimum. An example is finding the minimum cost path through a multistage graph.
Introduction to dynamic programming
Introduction to dynamic programmingAmisha Narsingani Dynamic programming is an algorithm design technique for optimization problems that reduces time by increasing space usage. It works by breaking problems down into overlapping subproblems and storing the solutions to subproblems, rather than recomputing them, to build up the optimal solution. The key aspects are identifying the optimal substructure of problems and handling overlapping subproblems in a bottom-up manner using tables. Examples that can be solved with dynamic programming include the knapsack problem, shortest paths, and matrix chain multiplication.
Dynamic pgmming
Dynamic pgmmingDr. C.V. Suresh Babu This document provides an overview of dynamic programming. It begins by explaining that dynamic programming is a technique for solving optimization problems by breaking them down into overlapping subproblems and storing the results of solved subproblems in a table to avoid recomputing them. It then provides examples of problems that can be solved using dynamic programming, including Fibonacci numbers, binomial coefficients, shortest paths, and optimal binary search trees. The key aspects of dynamic programming algorithms, including defining subproblems and combining their solutions, are also outlined.
ADA Unit 2.pptx
ADA Unit 2.pptxAmanKumar879992 The document discusses dynamic programming and how it can be used to calculate the 20th term of the Fibonacci sequence. Dynamic programming breaks problems down into overlapping subproblems, solves each subproblem once, and stores the results for future use. It explains that the Fibonacci sequence can be calculated recursively with each term equal to the sum of the previous two. To calculate the 20th term, dynamic programming would calculate each preceding term only once and store the results, building up the solution from previously solved subproblems until it reaches the 20th term.
Dynamic programming class 16
Dynamic programming class 16Kumar Dynamic programming is an algorithm design technique that solves problems by breaking them down into smaller overlapping subproblems and storing the results of already solved subproblems, rather than recomputing them. It is applicable to problems exhibiting optimal substructure and overlapping subproblems. The key steps are to define the optimal substructure, recursively define the optimal solution value, compute values bottom-up, and optionally reconstruct the optimal solution. Common examples that can be solved with dynamic programming include knapsack, shortest paths, matrix chain multiplication, and longest common subsequence.
Dynamic programming Basics
Dynamic programming BasicsKvishnu Dahatonde Dynamic programming (DP) addresses issues in divide and conquer by allowing inter-dependent sub-problems and avoiding re-computation through stored results. It prioritizes efficiency by computing smaller instances first and using previously computed solutions to inform subsequent calculations. Examples include solving the Fibonacci sequence and string edit problems, where DP significantly reduces time complexity compared to traditional recursive approaches.
L21_L27_Unit_5_Dynamic_Programming Computer Science
L21_L27_Unit_5_Dynamic_Programming Computer Sciencepriyanshukumarbt23cs L21_L27_Unit_5_Dynamic_Programming Computer Science and Engineering
Dynamic programming prasintation eaisy
Dynamic programming prasintation eaisyahmed51236 Dynamic programming is a technique for solving complex problems by breaking them down into simpler sub-problems. It involves storing solutions to sub-problems for later use, avoiding recomputing them. Examples where it can be applied include matrix chain multiplication and calculating Fibonacci numbers. For matrix chains, dynamic programming finds the optimal order for multiplying matrices with minimum computations. For Fibonacci numbers, it calculates values in linear time by storing previous solutions rather than exponentially recomputing them through recursion.
Chalukyas of Gujrat, Solanki Dynasty NEP.pptx
Chalukyas of Gujrat, Solanki Dynasty NEP.pptxDr. Ravi Shankar Arya Mahila P. G. College, Banaras Hindu University, Varanasi, India. This presentation has been made keeping in mind the students of undergraduate and postgraduate level. In this slide try to present the brief history of Chaulukyas of Gujrat up to Kumarpala To keep the facts in a natural form and to display the material in more detail, the help of various books, websites and online medium has been taken. Whatever medium the material or facts have been taken from, an attempt has been made by the presenter to give their reference at the end.
Chaulukya or Solanki was one of the Rajputs born from Agnikul. In the Vadnagar inscription, the origin of this dynasty is told from Brahma's Chauluk or Kamandalu. They ruled in Gujarat from the latter half of the tenth century to the beginning of the thirteenth century. Their capital was in Anahilwad. It is not certain whether it had any relation with the Chalukya dynasty of the south or not. It is worth mentioning that the name of the dynasty of the south was 'Chaluky' while the dynasty of Gujarat has been called 'Chaulukya'. The rulers of this dynasty were the supporters and patrons of Jainism.
VCE Literature Section A Exam Response Guide
VCE Literature Section A Exam Response Guidejpinnuck This practical guide shows students of Unit 3&4 VCE Literature how to write responses to Section A of the exam. Including a range of examples writing about different types of texts, this guide:
*Breaks down and explains what Q1 and Q2 tasks involve and expect
*Breaks down example responses for each question
*Explains and scaffolds students to write responses for each question
*Includes a comprehensive range of sentence starters and vocabulary for responding to each question
*Includes critical theory vocabulary lists to support Q2 responses
More Related Content
Similar to Dynamic Programming Algorithm CSI-504.pdf (20)
Annotaed slides for dynamic programming algorithm
Annotaed slides for dynamic programming algorithmjohnathangamal27 The document discusses the analysis and design of algorithms, focusing on dynamic programming as a strategy for solving optimization problems efficiently. It outlines key concepts, such as optimal substructure and overlapping subproblems, and provides examples like the Fibonacci sequence and longest common subsequence. The material also highlights algorithm design techniques, comparisons between recursive and dynamic programming solutions, and factors influencing time-space trade-offs.
Dynamic programming
Dynamic programmingYıldırım Tam A. Plannig
The document mentions that in dynamic programming, "Programming" refers to "planning", not computer programming.
Dynamic programming
Dynamic programmingMd.Mojibul Hoque Dynamic programming is an algorithm design technique for optimization problems that involves breaking down problems into sub-problems and storing solutions to sub-problems in tables to avoid recomputing them. It is used to minimize or maximize problems by combining solutions to sub-problems, with sub-problems potentially sharing sub-sub-problems. Dynamic programming reduces time complexity by increasing space complexity through storing optimal solutions to sub-problems in tables.
Dynamic programming
Dynamic programmingJay Nagar Dynamic programming is a method for solving complex problems by breaking them into simpler subproblems, solving each only once, and storing their solutions. It is applicable in optimization problems where optimal substructure and overlapping subproblems are present, allowing for efficient solutions compared to divide-and-conquer strategies. The process involves characterizing the optimal solution structure, defining the problem recursively, computing values systematically, and sometimes constructing the optimal solution based on these computed values.
Presentation (1) (5).pptx in DAA computer
Presentation (1) (5).pptx in DAA computerMadhurimaKomanduru The document presents an overview of dynamic programming, outlining its definition, method of operation, and applications in solving complex problems efficiently. Key applications discussed include the shortest path problem, knapsack problem, and the Floyd-Warshall algorithm, which is specifically highlighted for its simplicity and effectiveness in finding shortest paths in weighted graphs. The advantages of dynamic programming include its efficiency, ability to provide optimal solutions, and suitability for complex problem-solving.
Presentation (1) (5).pptx in DAA computer
Presentation (1) (5).pptx in DAA computerMadhurimaKomanduru The document discusses dynamic programming, an algorithmic technique for solving complex problems by dividing them into smaller overlapping subproblems. It covers applications such as the shortest path problem, knapsack problem, and the Travelling Salesman problem, also detailing the Floyd-Warshall algorithm for finding all pair shortest paths in a weighted graph. Advantages of dynamic programming include efficiency, optimal solutions, and memory efficiency.
Introduction to Dynamic Programming, Principle of Optimality
Introduction to Dynamic Programming, Principle of OptimalityBhavin Darji Dynamic programming is an optimization technique invented by Richard Bellman in 1950, designed to solve problems with overlapping subproblems by storing solutions to avoid recomputation. It involves characterizing the structure of optimal solutions, defining those solutions recursively, and computing the solutions either through top-down caching or a bottom-up approach. Applications of dynamic programming include problems like the knapsack problem, shortest path calculations, and matrix chain multiplication.
W8L1 Introduction & Fibonacci Numbers part 1.pptx
W8L1 Introduction & Fibonacci Numbers part 1.pptxsakibahmed181234 The document provides an introduction to dynamic programming, particularly focusing on solving the Fibonacci numbers problem. It discusses key components of dynamic programming, including optimal substructures, overlapping subproblems, and the importance of tabular computation to avoid redundant calculations. The algorithm's structure involves characterizing optimal solutions, defining their values recursively, and constructing solutions in a bottom-up manner.
Dynamic programming in Design Analysis and Algorithms
Dynamic programming in Design Analysis and AlgorithmsNikunjGoyal20 Dynamic programming is an optimization approach that breaks down problems into overlapping sub-problems whose results are reused (memoization). It contrasts with divide and conquer by relying on previous outputs to optimize larger sub-problems, with two main approaches: top-down (recursive with memoization) and bottom-up (iterative with tabulation). Key applications include the Fibonacci series, knapsack problem, and Bellman-Ford algorithm for shortest paths.
Dynamic Programming
Dynamic Programmingparamalways Dynamic programming is a recursive optimization technique used to solve problems with interrelated decisions. It breaks the problem down into sequential steps, where each step builds on the solutions to previous steps. The optimal solution is determined by working through each step in order. Dynamic programming has advantages like computational savings over complete enumeration and providing insight into problem nature. However, it also has disadvantages like requiring more expertise, lacking general algorithms, and facing dimensionality problems for applications with multiple states.
Dynamic Programming.pptx
Dynamic Programming.pptxMuktarHossain13 Dynamic programming is a technique that breaks problems into subproblems and saves results to optimize solutions without recomputing subproblems. It is commonly used in computer science, mathematics, economics, and operations research for problems like the Fibonacci series, knapsack problem, and traveling salesman problem. Dynamic programming improves efficiency by storing subproblem solutions and avoiding redundant calculations. It can find optimal and approximate solutions to large problems. For the Fibonacci series, a dynamic programming approach builds up the sequence by calculating each term from the previous two terms rather than recursively calculating all subproblems.
Pintu ram
Pintu rampinturam2 Dynamic programming is an algorithm design technique for solving complex problems defined by overlapping subproblems. It works by solving each subproblem only once and storing the results for future use, avoiding the work of recomputing results. The principle of optimality states that optimal solutions can be constructed from optimal solutions of subproblems. Dynamic programming algorithms involve characterizing the optimal substructure, recursively defining the optimal value, computing the optimal value in a bottom-up manner, and constructing an optimal solution. It is generally applied to optimization problems and provides efficient solutions, though it may not always find a global optimum. An example is finding the minimum cost path through a multistage graph.
Introduction to dynamic programming
Introduction to dynamic programmingAmisha Narsingani Dynamic programming is an algorithm design technique for optimization problems that reduces time by increasing space usage. It works by breaking problems down into overlapping subproblems and storing the solutions to subproblems, rather than recomputing them, to build up the optimal solution. The key aspects are identifying the optimal substructure of problems and handling overlapping subproblems in a bottom-up manner using tables. Examples that can be solved with dynamic programming include the knapsack problem, shortest paths, and matrix chain multiplication.
Dynamic pgmming
Dynamic pgmmingDr. C.V. Suresh Babu This document provides an overview of dynamic programming. It begins by explaining that dynamic programming is a technique for solving optimization problems by breaking them down into overlapping subproblems and storing the results of solved subproblems in a table to avoid recomputing them. It then provides examples of problems that can be solved using dynamic programming, including Fibonacci numbers, binomial coefficients, shortest paths, and optimal binary search trees. The key aspects of dynamic programming algorithms, including defining subproblems and combining their solutions, are also outlined.
ADA Unit 2.pptx
ADA Unit 2.pptxAmanKumar879992 The document discusses dynamic programming and how it can be used to calculate the 20th term of the Fibonacci sequence. Dynamic programming breaks problems down into overlapping subproblems, solves each subproblem once, and stores the results for future use. It explains that the Fibonacci sequence can be calculated recursively with each term equal to the sum of the previous two. To calculate the 20th term, dynamic programming would calculate each preceding term only once and store the results, building up the solution from previously solved subproblems until it reaches the 20th term.
Dynamic programming class 16
Dynamic programming class 16Kumar Dynamic programming is an algorithm design technique that solves problems by breaking them down into smaller overlapping subproblems and storing the results of already solved subproblems, rather than recomputing them. It is applicable to problems exhibiting optimal substructure and overlapping subproblems. The key steps are to define the optimal substructure, recursively define the optimal solution value, compute values bottom-up, and optionally reconstruct the optimal solution. Common examples that can be solved with dynamic programming include knapsack, shortest paths, matrix chain multiplication, and longest common subsequence.
Dynamic programming Basics
Dynamic programming BasicsKvishnu Dahatonde Dynamic programming (DP) addresses issues in divide and conquer by allowing inter-dependent sub-problems and avoiding re-computation through stored results. It prioritizes efficiency by computing smaller instances first and using previously computed solutions to inform subsequent calculations. Examples include solving the Fibonacci sequence and string edit problems, where DP significantly reduces time complexity compared to traditional recursive approaches.
L21_L27_Unit_5_Dynamic_Programming Computer Science
L21_L27_Unit_5_Dynamic_Programming Computer Sciencepriyanshukumarbt23cs L21_L27_Unit_5_Dynamic_Programming Computer Science and Engineering
Dynamic programming prasintation eaisy
Dynamic programming prasintation eaisyahmed51236 Dynamic programming is a technique for solving complex problems by breaking them down into simpler sub-problems. It involves storing solutions to sub-problems for later use, avoiding recomputing them. Examples where it can be applied include matrix chain multiplication and calculating Fibonacci numbers. For matrix chains, dynamic programming finds the optimal order for multiplying matrices with minimum computations. For Fibonacci numbers, it calculates values in linear time by storing previous solutions rather than exponentially recomputing them through recursion.
Recently uploaded (20)
Chalukyas of Gujrat, Solanki Dynasty NEP.pptx
Chalukyas of Gujrat, Solanki Dynasty NEP.pptxDr. Ravi Shankar Arya Mahila P. G. College, Banaras Hindu University, Varanasi, India. This presentation has been made keeping in mind the students of undergraduate and postgraduate level. In this slide try to present the brief history of Chaulukyas of Gujrat up to Kumarpala To keep the facts in a natural form and to display the material in more detail, the help of various books, websites and online medium has been taken. Whatever medium the material or facts have been taken from, an attempt has been made by the presenter to give their reference at the end.
Chaulukya or Solanki was one of the Rajputs born from Agnikul. In the Vadnagar inscription, the origin of this dynasty is told from Brahma's Chauluk or Kamandalu. They ruled in Gujarat from the latter half of the tenth century to the beginning of the thirteenth century. Their capital was in Anahilwad. It is not certain whether it had any relation with the Chalukya dynasty of the south or not. It is worth mentioning that the name of the dynasty of the south was 'Chaluky' while the dynasty of Gujarat has been called 'Chaulukya'. The rulers of this dynasty were the supporters and patrons of Jainism.
VCE Literature Section A Exam Response Guide
VCE Literature Section A Exam Response Guidejpinnuck This practical guide shows students of Unit 3&4 VCE Literature how to write responses to Section A of the exam. Including a range of examples writing about different types of texts, this guide:
*Breaks down and explains what Q1 and Q2 tasks involve and expect
*Breaks down example responses for each question
*Explains and scaffolds students to write responses for each question
*Includes a comprehensive range of sentence starters and vocabulary for responding to each question
*Includes critical theory vocabulary lists to support Q2 responses
ECONOMICS, DISASTER MANAGEMENT, ROAD SAFETY - STUDY MATERIAL [10TH]
ECONOMICS, DISASTER MANAGEMENT, ROAD SAFETY - STUDY MATERIAL [10TH]SHERAZ AHMAD LONE This study material for Class 10th covers the core subjects of Economics, Disaster Management, and Road Safety Education, developed strictly in line with the JKBOSE textbook. It presents the content in a simplified, structured, and student-friendly format, ensuring clarity in concepts. The material includes reframed explanations, flowcharts, infographics, and key point summaries to support better understanding and retention. Designed for classroom teaching and exam preparation, it aims to enhance comprehension, critical thinking, and practical awareness among students.
List View Components in Odoo 18 - Odoo Slides
List View Components in Odoo 18 - Odoo SlidesCeline George In Odoo, there are many types of views possible like List view, Kanban view, Calendar view, Pivot view, Search view, etc.
The major change that introduced in the Odoo 18 technical part in creating views is the tag <tree> got replaced with the <list> for creating list views.
Code Profiling in Odoo 18 - Odoo 18 Slides
Code Profiling in Odoo 18 - Odoo 18 SlidesCeline George Profiling in Odoo identifies slow code and resource-heavy processes, ensuring better system performance. Odoo code profiling detects bottlenecks in custom modules, making it easier to improve speed and scalability.
INDUCTIVE EFFECT slide for first prof pharamacy students
INDUCTIVE EFFECT slide for first prof pharamacy studentsSHABNAM FAIZ The inductive effect is the electron-withdrawing or electron-donating effect transmitted through sigma (σ) bonds in a molecule due to differences in electronegativity between atoms.
---
🔹 Definition:
The inductive effect is the permanent shifting of electrons in a sigma bond caused by the electronegativity difference of atoms, resulting in partial charges within the molecule.
How to Customize Quotation Layouts in Odoo 18
How to Customize Quotation Layouts in Odoo 18Celine George Customizing quotation layouts in Odoo 18 allows businesses to personalize their quotations to match branding or specific requirements. This can include adding logos, custom fields, or modifying headers and footers.
Aprendendo Arquitetura Framework Salesforce - Dia 02
Aprendendo Arquitetura Framework Salesforce - Dia 02Mauricio Alexandre Silva Aprendendo Arquitetura Framework Salesforce - Dia 02
YSPH VMOC Special Report - Measles Outbreak Southwest US 6-14-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 6-14-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC) BLUF:
The Texas outbreak has slowed down, but sporadic cases continue to emerge in Kansas, Oklahoma, and New Mexico.
Elsewhere in the US, we continue to see signs of acceleration due to outbreaks outside the Southwest (North Dakota, Montana, and Colorado) and travel-related cases. Measles exposures due to travel are expected to pose a significant challenge throughout the summer.
The U.S. is on track to exceed its 30-year high for measles cases (1,274) within the next two weeks.
Here is the latest update:
CURRENT CASE COUNT: 919
•Texas: 744 (+2) (55% of cases are in Gaines County).
•New Mexico: 81 (83% of cases are from Lea County).
•Oklahoma: 20 (+2)
•Kansas: 74 (+5) (38.89% of the cases are from Gray County).
HOSPITALIZATIONS: 104
• Texas: 96 (+2) – This accounts for 13% of all cases in Texas.
• New Mexico: 7 – This accounts for 9.47% of all cases in New Mexico.
• Kansas: 3 – This accounts for 5.08% of all cases in the state of Kansas.
DEATHS: 3
•Texas: 2 – This is 0.27% of all cases in Texas.
•New Mexico: 1 – This is 1.23% of all cases in New Mexico.
US NATIONAL CASE COUNT: 1,197
INTERNATIONAL SPREAD
•Mexico: 2337 (+257), 5 fatalities
‒Chihuahua, Mexico: 2,179 (+239) cases, 4 fatalities, 7 currently hospitalized.
•Canada: 3,207 (+208), 1 fatality
‒Ontario Outbreak, Canada: 2,115 (+74) cases, 158 hospitalizations, 1 fatality.
‒Alberta, Canada: 879(+118) cases, 5 currently hospitalized.
Pests of Maize: An comprehensive overview.pptx
Pests of Maize: An comprehensive overview.pptxArshad Shaikh Maize is susceptible to various pests that can significantly impact yields. Key pests include the fall armyworm, stem borers, cob earworms, shoot fly. These pests can cause extensive damage, from leaf feeding and stalk tunneling to grain destruction. Effective management strategies, such as integrated pest management (IPM), resistant varieties, biological control, and judicious use of chemicals, are essential to mitigate losses and ensure sustainable maize production.
LDMMIA Shop & Student News Summer Solstice 25
LDMMIA Shop & Student News Summer Solstice 25LDM & Mia eStudios 6/18/25
Shop, Upcoming: Final Notes to Review as we Close Level One. Make sure to review the orientation and videos as well. There’s more to come and material to cover in Levels 2-3. The content will be a combination of Reiki and Yoga. Also energy topics of our spiritual collective.
Thanks again all future Practitioner Level Students. Our Levels so far are: Guest, Grad, and Practitioner. We have had over 5k Spring Views.
https://ldm-mia.creator-spring.com
Filipino 9 Maikling Kwento Ang Ama Panitikang Asiyano
Filipino 9 Maikling Kwento Ang Ama Panitikang Asiyanosumadsadjelly121997 Filipino 9 Maikling Kwento Ang Ama Panitikang Asiyano
How payment terms are configured in Odoo 18
How payment terms are configured in Odoo 18Celine George Payment terms in Odoo 18 help define the conditions for when invoices are due. This feature can split payments into multiple parts and automate due dates based on specific rules.
Great Governors' Send-Off Quiz 2025 Prelims IIT KGP
Great Governors' Send-Off Quiz 2025 Prelims IIT KGPIIT Kharagpur Quiz Club Prelims of the Great Governors' Send-Off Quiz 2025 hosted by the outgoing governors.
QMs: Aarushi, Aatir, Aditya, Arnav
Romanticism in Love and Sacrifice An Analysis of Oscar Wilde’s The Nightingal...
Romanticism in Love and Sacrifice An Analysis of Oscar Wilde’s The Nightingal...KaryanaTantri21 The story revolves around a college student who despairs not having a red rose as a condition for dancing with the girl he loves. The nightingale hears his complaint and offers to create the red rose at the cost of his life. He sang a love song all night with his chest stuck to the thorns of the rose tree. Finally, the red rose grew, but his sacrifice was in vain. The girl rejected the flower because it didn’t match her outfit and preferred a jewellery gift. The student threw the flower on the street and returned to studying philosophy
K12 Tableau User Group virtual event June 18, 2025
K12 Tableau User Group virtual event June 18, 2025dogden2 National K12 Tableau User Group: June 2025 meeting slides
Chalukyas of Gujrat, Solanki Dynasty NEP.pptx
Chalukyas of Gujrat, Solanki Dynasty NEP.pptxDr. Ravi Shankar Arya Mahila P. G. College, Banaras Hindu University, Varanasi, India.
YSPH VMOC Special Report - Measles Outbreak Southwest US 6-14-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 6-14-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)
Ad
Dynamic Programming Algorithm CSI-504.pdf
- 1. Dynamic Programming Algorithm Presented by 602, 629, 630, 646
- 2. What is Dynamic Programming (DP) Algorithm Dynamic Programming (DP) is a method used in mathematics and computer science to solve complex problems by breaking them down into simpler subproblems. By solving each subproblem only once and storing the results, it avoids redundant computations, leading to more efficient solutions for a wide range of problems.
- 3. How does DP works • Identify Subproblems: Divide the main problem into smaller, independent subproblems. • Store Solutions: Solve each subproblem and store the solution in a table or array. • Build Up Solutions: Use the stored solutions to build up the solution to the main problem. • Avoid Redundancy: By storing solutions, DP ensures that each subproblem is solved only once, reducing computation time.
- 4. Example of Dynamic Programming (DP) Example 1: Consider the problem of finding the Fibonacci sequence: Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, … Brute Force Approach: To find the nth Fibonacci number using a brute force approach, you would simply add the (n-1)th and (n-2)th Fibonacci numbers. This would work, but it would be inefficient for large values of n, as it would require calculating all the previous Fibonacci numbers.
- 5. F(0) = 0 F(1) = 1 F(n) = F(n-1) + F(n-2)
- 6. Fibonacci Series using Dynamic Programming Subproblems: F(0), F(1), F(2), F(3), … Store Solutions: Create a table to store the values of F(n) as they are calculated. Build Up Solutions: For F(n), look up F(n-1) and F(n-2) in the table and add them. Avoid Redundancy: The table ensures that each subproblem (e.g., F(2)) is solved only once. By using DP, we can efficiently calculate the Fibonacci sequence without having to recompute subproblems.
- 7. Approaches of Dynamic Programming (DP) Dynamic programming can be achieved using two approaches: 01. Top-Down Approach 02. Bottom-Down Approach
- 8. Top-Down Approach In the top-down approach, also known as memoization, we start with the final solution and recursively break it down into smaller subproblems. To avoid redundant calculations, we store the results of solved subproblems in a memoization table. Let’s breakdown Top down approach: • Starts with the final solution and recursively breaks it down into smaller subproblems. • Stores the solutions to subproblems in a table to avoid redundant calculations. • Suitable when the number of subproblems is large and many of them are reused.
- 9. Bottom-Down Approach In the bottom-up approach, also known as tabulation, we start with the smallest subproblems and gradually build up to the final solution. We store the results of solved subproblems in a table to avoid redundant calculations. Let’s breakdown Bottom-up approach: • Starts with the smallest subproblems and gradually builds up to the final solution. • Fills a table with solutions to subproblems in a bottom-up manner. • Suitable when the number of subproblems is small and the optimal solution can be directly computed from the solutions to smaller subproblems.
- 10. Applications: Dynamic programming has a wide range of applications, including: • Optimization: Knapsack problem, shortest path problem, maximum subarray problem • Computer Science: Longest common subsequence, edit distance, string matching • Operations Research: Inventory management, scheduling, resource allocation