Measuring the performance of algorithm, in terms of Big(O), theta, and Omega notation with respect to their Scientific notation like Worst, Average, Best, cases
This document discusses active databases and database triggers. It defines a trigger as a procedure that is automatically invoked by the database management system in response to specified changes made to the database. An active database is one that has associated triggers. Triggers have three parts - an event that activates the trigger, an optional condition, and an action that is executed if the condition evaluates to true. Triggers allow maintaining database integrity and performing additional actions in response to insert, update, or delete statements. They can also be used for auditing and logging changes made to the database.
how to calclute time complexity of algortihmSajid Marwat
This document discusses algorithm analysis and complexity. It defines key terms like asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like a sum function to determine their time complexity. Common analyses include looking at loops, nested loops, and sequences of statements. The goal is to classify algorithms according to their complexity, which is important for large inputs and machine-independent. Algorithms are classified based on worst, average, and best case analyses.
This document defines and describes trees and graphs as non-linear data structures. It explains that a tree is similar to a linked list but allows nodes to have multiple children rather than just one. The document defines key tree terms like height, ancestors, size, and different types of binary trees including strict, full, and complete. It provides properties of binary trees such as the number of nodes in full and complete binary trees based on height.
The document provides information on various sorting and searching algorithms, including bubble sort, insertion sort, selection sort, quick sort, sequential search, and binary search. It includes pseudocode to demonstrate the algorithms and example implementations with sample input data. Key points covered include the time complexity of each algorithm (O(n^2) for bubble/insertion/selection sort, O(n log n) for quick sort, O(n) for sequential search, and O(log n) for binary search) and how they work at a high level.
Time-space tradeoffs allow solving problems in less time by using more memory or solving problems using very little space by spending more time. Common tradeoffs include storing compressed vs uncompressed data, re-rendering images vs storing pre-rendered images, using smaller code with loops vs larger code without loops, and storing lookup tables vs recalculating values. Examples demonstrate algorithms that use more time and less space vs more space and less time.
The document discusses optimal binary search trees (OBST) and describes the process of creating one. It begins by introducing OBST and noting that the method can minimize average number of comparisons in a successful search. It then shows the step-by-step process of calculating the costs for different partitions to arrive at the optimal binary search tree for a given sample dataset with keys and frequencies. The process involves calculating Catalan numbers for each partition and choosing the minimum cost at each step as the optimal is determined.
The Theta (Θ) notation is a method of expressing the asymptotic tight bound on the growth rate of an
algorithm’s running time both from above and below ends i.e. upper bound and lower bound.
Operating system - Process and its conceptsKaran Thakkar
This presentation gives an overview of Process concepts in Operating System. The presentation aims at alleviating most of the overheads while understanding the process concept in operating system. this tailor made presentation will help individuals to understand the overall meaning of process and its underlying concepts used in an operating system.
This document discusses active databases and database triggers. It defines a trigger as a procedure that is automatically invoked by the database management system in response to specified changes made to the database. An active database is one that has associated triggers. Triggers have three parts - an event that activates the trigger, an optional condition, and an action that is executed if the condition evaluates to true. Triggers allow maintaining database integrity and performing additional actions in response to insert, update, or delete statements. They can also be used for auditing and logging changes made to the database.
how to calclute time complexity of algortihmSajid Marwat
This document discusses algorithm analysis and complexity. It defines key terms like asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like a sum function to determine their time complexity. Common analyses include looking at loops, nested loops, and sequences of statements. The goal is to classify algorithms according to their complexity, which is important for large inputs and machine-independent. Algorithms are classified based on worst, average, and best case analyses.
This document defines and describes trees and graphs as non-linear data structures. It explains that a tree is similar to a linked list but allows nodes to have multiple children rather than just one. The document defines key tree terms like height, ancestors, size, and different types of binary trees including strict, full, and complete. It provides properties of binary trees such as the number of nodes in full and complete binary trees based on height.
The document provides information on various sorting and searching algorithms, including bubble sort, insertion sort, selection sort, quick sort, sequential search, and binary search. It includes pseudocode to demonstrate the algorithms and example implementations with sample input data. Key points covered include the time complexity of each algorithm (O(n^2) for bubble/insertion/selection sort, O(n log n) for quick sort, O(n) for sequential search, and O(log n) for binary search) and how they work at a high level.
Time-space tradeoffs allow solving problems in less time by using more memory or solving problems using very little space by spending more time. Common tradeoffs include storing compressed vs uncompressed data, re-rendering images vs storing pre-rendered images, using smaller code with loops vs larger code without loops, and storing lookup tables vs recalculating values. Examples demonstrate algorithms that use more time and less space vs more space and less time.
The document discusses optimal binary search trees (OBST) and describes the process of creating one. It begins by introducing OBST and noting that the method can minimize average number of comparisons in a successful search. It then shows the step-by-step process of calculating the costs for different partitions to arrive at the optimal binary search tree for a given sample dataset with keys and frequencies. The process involves calculating Catalan numbers for each partition and choosing the minimum cost at each step as the optimal is determined.
The Theta (Θ) notation is a method of expressing the asymptotic tight bound on the growth rate of an
algorithm’s running time both from above and below ends i.e. upper bound and lower bound.
Operating system - Process and its conceptsKaran Thakkar
This presentation gives an overview of Process concepts in Operating System. The presentation aims at alleviating most of the overheads while understanding the process concept in operating system. this tailor made presentation will help individuals to understand the overall meaning of process and its underlying concepts used in an operating system.
The document discusses assembly language programming concepts including the stack segment, stack, stack instructions, subroutines, macros, and recursive procedures. It provides examples and explanations of these concepts. It also includes sample programs and solutions related to stacks, subroutines, and other assembly language topics.
The document discusses stream ciphers and how they can be implemented in either hardware or software. It describes how stream ciphers work by generating a pseudorandom bitstream from a key and nonce that is XOR'd with the plaintext. Hardware-oriented stream ciphers were initially more efficient to implement than block ciphers using dedicated circuits like LFSRs. However, LFSR-based designs are insecure and modern software-oriented stream ciphers like Salsa20 are more efficient on CPUs. The document cautions that stream ciphers can be broken if the key and nonce are reused or if there are flaws in the implementation.
Memory management is the act of managing computer memory. The essential requirement of memory management is to provide ways to dynamically allocate portions of memory to programs at their request, and free it for reuse when no longer needed. This is critical to any advanced computer system where more than a single process might be underway at any time
The document discusses run-time environments and how compilers support program execution through run-time environments. It covers:
1) The compiler cooperates with the OS and system software through a run-time environment to implement language abstractions during execution.
2) The run-time environment handles storage layout/allocation, variable access, procedure linkage, parameter passing and interfacing with the OS.
3) Memory is typically divided into code, static storage, heap and stack areas, with the stack and heap growing towards opposite ends of memory dynamically during execution.
This document discusses runtime environments and storage allocation strategies. It covers:
- How procedure activations are represented at runtime using activation records, control stacks, and activation trees. Activation records store local variables, parameters, return values, and more.
- Different strategies for allocating storage at runtime, including static allocation where sizes are known at compile time, stack allocation for procedure activations and recursion, and heap allocation for dynamic memory.
- How names are bound to values at compile time through environments and at runtime through states. The scope and lifetime of bindings are also discussed.
- Issues related to mapping names to storage locations and values at runtime, including how assignments change the state but not the environment.
-
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
The document discusses query optimization by describing how a database system estimates the cost of different query evaluation plans using statistical information about relations. It covers topics like estimating the size of selections, joins, aggregations and other operations to choose the lowest cost plan using transformations and equivalence rules.
Presentation On RAID(Redundant Array Of Independent Disks) BasicsKuber Chandra
This document discusses RAID (Redundant Array of Independent Disks) configurations and their uses. It describes several common RAID types (RAID 0, 1, 5, 10), explaining their characteristics like performance, redundancy, and storage efficiency. Software and hardware implementations of RAID are also overviewed. The document concludes by looking at emerging technologies like RAID 6 and potential future directions such as improved rebuild times and predictive drive failure detection.
This document discusses real-time scheduling algorithms. It begins by defining real-time systems and their key properties of timeliness and predictability. It then discusses two common real-time scheduling algorithms: fixed-priority Rate Monotonic scheduling and dynamic-priority Earliest Deadline First scheduling. It covers how each algorithm prioritizes and orders tasks, and analyzes their schedulability and utilization bounds. It concludes by comparing the two approaches.
This document discusses parsing and context-free grammars. It defines parsing as verifying that tokens generated by a lexical analyzer follow syntactic rules of a language using a parser. Context-free grammars are defined using terminals, non-terminals, productions and a start symbol. Top-down and bottom-up parsing are introduced. Techniques for grammar analysis and improvement like left factoring, eliminating left recursion, calculating first and follow sets are explained with examples.
The document discusses first-order logic (FOL) and its advantages over propositional logic for representing knowledge. It introduces the basic elements of FOL syntax, such as constants, predicates, functions, variables, and connectives. It provides examples of FOL expressions and discusses how objects and relations between objects can be represented. It also covers quantification in FOL using universal and existential quantifiers.
This document summarizes the key components of a microprogrammed computer system, including the control memory, registers, instruction format, microinstruction format, and microoperations. It provides details on how a microprogram is used to define the bit values for each of the 128 words in the control memory to implement routines for instructions like fetch, decode, and execution. The symbolic microprogram needs to be translated to binary for storage in the control memory.
Log based and Recovery with concurrent transactionnikunjandy
The document describes log-based recovery techniques for databases. It discusses two techniques - deferred database modification and immediate database modification. For deferred modification, writes are deferred until after commit. For immediate modification, writes can occur before commit as long as the log record is written first. The document also covers recovery with concurrent transactions using undo/redo lists and checkpoints.
- Recurrences describe functions in terms of their values on smaller inputs and arise when algorithms contain recursive calls to themselves.
- To analyze the running time of recursive algorithms, the recurrence must be solved to find an explicit formula or bound the expression in terms of n.
- Examples of recurrences and their solutions are given, including binary search (O(log n)), dividing the input in half at each step (O(n)), and dividing the input in half but examining all items (O(n)).
- Methods for solving recurrences include iteration, substitution, and using recursion trees to "guess" the solution.
Deadlock is a very important topic in operating system. In this presentation slide, try to relate deadlock with real life scenario and find out some solution with two main algorithm- Safety and Banker's Algorithm.
The document discusses different methods of organizing computer files, including heap files, sequential files, indexed-sequential files, inverted list files, and direct files. It provides details on each method, such as how records are stored and accessed, their advantages and disadvantages, and examples. Key aspects covered include unordered storage in heap files, ordered storage and efficient sequential access in sequential files, indexed access for both sequential and random access in indexed-sequential files, and direct calculation of record locations in direct files.
This document discusses asymptotic analysis and recurrence relations. It begins by introducing asymptotic notations like Big O, Omega, and Theta notation that are used to analyze algorithms. It then discusses recurrence relations, which express the running time of algorithms in terms of input size. The document provides examples of using recurrence relations to find the time complexity of algorithms like merge sort. It also discusses how to calculate time complexity functions like f(n) asymptotically rather than calculating exact running times. The goal of this analysis is to understand how algorithm running times scale with input size.
Performance analysis is important for algorithms and software features. Asymptotic analysis evaluates how an algorithm's time or space requirements grow with increasing input size, ignoring constants and machine-specific factors. This allows algorithms to be analyzed and compared regardless of machine or small inputs. The document discusses common time complexities like O(1), O(n), O(n log n), and analyzing worst, average, and best cases. It also covers techniques like recursion, amortized analysis, and the master method for solving algorithm recurrences.
The document discusses assembly language programming concepts including the stack segment, stack, stack instructions, subroutines, macros, and recursive procedures. It provides examples and explanations of these concepts. It also includes sample programs and solutions related to stacks, subroutines, and other assembly language topics.
The document discusses stream ciphers and how they can be implemented in either hardware or software. It describes how stream ciphers work by generating a pseudorandom bitstream from a key and nonce that is XOR'd with the plaintext. Hardware-oriented stream ciphers were initially more efficient to implement than block ciphers using dedicated circuits like LFSRs. However, LFSR-based designs are insecure and modern software-oriented stream ciphers like Salsa20 are more efficient on CPUs. The document cautions that stream ciphers can be broken if the key and nonce are reused or if there are flaws in the implementation.
Memory management is the act of managing computer memory. The essential requirement of memory management is to provide ways to dynamically allocate portions of memory to programs at their request, and free it for reuse when no longer needed. This is critical to any advanced computer system where more than a single process might be underway at any time
The document discusses run-time environments and how compilers support program execution through run-time environments. It covers:
1) The compiler cooperates with the OS and system software through a run-time environment to implement language abstractions during execution.
2) The run-time environment handles storage layout/allocation, variable access, procedure linkage, parameter passing and interfacing with the OS.
3) Memory is typically divided into code, static storage, heap and stack areas, with the stack and heap growing towards opposite ends of memory dynamically during execution.
This document discusses runtime environments and storage allocation strategies. It covers:
- How procedure activations are represented at runtime using activation records, control stacks, and activation trees. Activation records store local variables, parameters, return values, and more.
- Different strategies for allocating storage at runtime, including static allocation where sizes are known at compile time, stack allocation for procedure activations and recursion, and heap allocation for dynamic memory.
- How names are bound to values at compile time through environments and at runtime through states. The scope and lifetime of bindings are also discussed.
- Issues related to mapping names to storage locations and values at runtime, including how assignments change the state but not the environment.
-
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
The document discusses query optimization by describing how a database system estimates the cost of different query evaluation plans using statistical information about relations. It covers topics like estimating the size of selections, joins, aggregations and other operations to choose the lowest cost plan using transformations and equivalence rules.
Presentation On RAID(Redundant Array Of Independent Disks) BasicsKuber Chandra
This document discusses RAID (Redundant Array of Independent Disks) configurations and their uses. It describes several common RAID types (RAID 0, 1, 5, 10), explaining their characteristics like performance, redundancy, and storage efficiency. Software and hardware implementations of RAID are also overviewed. The document concludes by looking at emerging technologies like RAID 6 and potential future directions such as improved rebuild times and predictive drive failure detection.
This document discusses real-time scheduling algorithms. It begins by defining real-time systems and their key properties of timeliness and predictability. It then discusses two common real-time scheduling algorithms: fixed-priority Rate Monotonic scheduling and dynamic-priority Earliest Deadline First scheduling. It covers how each algorithm prioritizes and orders tasks, and analyzes their schedulability and utilization bounds. It concludes by comparing the two approaches.
This document discusses parsing and context-free grammars. It defines parsing as verifying that tokens generated by a lexical analyzer follow syntactic rules of a language using a parser. Context-free grammars are defined using terminals, non-terminals, productions and a start symbol. Top-down and bottom-up parsing are introduced. Techniques for grammar analysis and improvement like left factoring, eliminating left recursion, calculating first and follow sets are explained with examples.
The document discusses first-order logic (FOL) and its advantages over propositional logic for representing knowledge. It introduces the basic elements of FOL syntax, such as constants, predicates, functions, variables, and connectives. It provides examples of FOL expressions and discusses how objects and relations between objects can be represented. It also covers quantification in FOL using universal and existential quantifiers.
This document summarizes the key components of a microprogrammed computer system, including the control memory, registers, instruction format, microinstruction format, and microoperations. It provides details on how a microprogram is used to define the bit values for each of the 128 words in the control memory to implement routines for instructions like fetch, decode, and execution. The symbolic microprogram needs to be translated to binary for storage in the control memory.
Log based and Recovery with concurrent transactionnikunjandy
The document describes log-based recovery techniques for databases. It discusses two techniques - deferred database modification and immediate database modification. For deferred modification, writes are deferred until after commit. For immediate modification, writes can occur before commit as long as the log record is written first. The document also covers recovery with concurrent transactions using undo/redo lists and checkpoints.
- Recurrences describe functions in terms of their values on smaller inputs and arise when algorithms contain recursive calls to themselves.
- To analyze the running time of recursive algorithms, the recurrence must be solved to find an explicit formula or bound the expression in terms of n.
- Examples of recurrences and their solutions are given, including binary search (O(log n)), dividing the input in half at each step (O(n)), and dividing the input in half but examining all items (O(n)).
- Methods for solving recurrences include iteration, substitution, and using recursion trees to "guess" the solution.
Deadlock is a very important topic in operating system. In this presentation slide, try to relate deadlock with real life scenario and find out some solution with two main algorithm- Safety and Banker's Algorithm.
The document discusses different methods of organizing computer files, including heap files, sequential files, indexed-sequential files, inverted list files, and direct files. It provides details on each method, such as how records are stored and accessed, their advantages and disadvantages, and examples. Key aspects covered include unordered storage in heap files, ordered storage and efficient sequential access in sequential files, indexed access for both sequential and random access in indexed-sequential files, and direct calculation of record locations in direct files.
This document discusses asymptotic analysis and recurrence relations. It begins by introducing asymptotic notations like Big O, Omega, and Theta notation that are used to analyze algorithms. It then discusses recurrence relations, which express the running time of algorithms in terms of input size. The document provides examples of using recurrence relations to find the time complexity of algorithms like merge sort. It also discusses how to calculate time complexity functions like f(n) asymptotically rather than calculating exact running times. The goal of this analysis is to understand how algorithm running times scale with input size.
Performance analysis is important for algorithms and software features. Asymptotic analysis evaluates how an algorithm's time or space requirements grow with increasing input size, ignoring constants and machine-specific factors. This allows algorithms to be analyzed and compared regardless of machine or small inputs. The document discusses common time complexities like O(1), O(n), O(n log n), and analyzing worst, average, and best cases. It also covers techniques like recursion, amortized analysis, and the master method for solving algorithm recurrences.
The document discusses complexity analysis of algorithms. It defines time complexity as the calculation of the total time required for an algorithm to execute, and space complexity as the calculation of memory space required. Time and space complexity can be analyzed using asymptotic analysis, which studies how performance changes with increasing input size. Asymptotic notations like Big-O, Omega, and Theta are used to analyze best case, worst case, and average case time complexity. Big-O notation represents upper time bound, Omega lower time bound, and Theta both upper and lower time bound. Examples are given of functions and their time complexities using these notations.
This document provides an overview of algorithm analysis and asymptotic complexity. It discusses learning outcomes related to analyzing algorithm efficiency using Big O, Omega, and Theta notation. Key points covered include:
- Defining the problem size n and relating algorithm running time to n
- Distinguishing between best-case, worst-case, and average-case complexity
- Using asymptotic notation like Big O to give upper bounds on complexity rather than precise calculations
- Common asymptotic categories like O(n), O(n^2), O(n log n) that classify algorithm growth rates
TIME EXECUTION OF DIFFERENT SORTED ALGORITHMSTanya Makkar
what is Algorithm and classification and its complexity
Time Complexity
Time Space trade-off
Asymptotic time complexity of algorithm and its notation
Why do we need to classify running time of algorithm into growth rates?
Big O-h notation and example
Big omega notation and example
Big theta notation and its example
best among the 3 notation
finding complexity f(n) for certain cases
1. Average case
2.Best case
3.Worst case
Searching
Sorting
complexity of Sorting
Conclusion
Data Structures and Algorithms Lecture 2: Analysis of Algorithms, Asymptotic ...TechVision8
This document discusses analyzing the running time of algorithms. It introduces pseudocode as a way to describe algorithms, primitive operations that are used to count the number of basic steps an algorithm takes, and asymptotic analysis to determine an algorithm's growth rate as the input size increases. The key points covered are using big-O notation to focus on the dominant term and ignore lower-order terms and constants, and analyzing two algorithms for computing prefix averages to demonstrate asymptotic analysis.
The document discusses algorithms, data abstraction, asymptotic analysis, arrays, polynomials, and sparse matrices. It defines algorithms and discusses their advantages and disadvantages. It explains how to design an algorithm and describes iterative and recursive algorithms. It defines data abstraction and gives an example using smartphones. It discusses time and space complexity analysis and different asymptotic notations like Big O, Omega, and Theta. It describes what arrays are, different types of arrays, and applications of arrays. It explains how to represent and add polynomials using linked lists. Finally, it defines sparse matrices and two methods to represent them using arrays and linked lists.
The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts such as algorithms, running time analysis, experimental studies vs theoretical analysis, pseudocode, primitive operations, counting operations, big-O notation, and analyzing algorithms to determine asymptotic running time. As an example, it analyzes two algorithms for computing prefix averages - one with quadratic running time O(n^2) and one with linear running time O(n).
The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts such as algorithms, running time analysis, experimental studies vs theoretical analysis, pseudocode, primitive operations, counting operations, big-O notation, and analyzing algorithms to determine asymptotic running time. As an example, it analyzes two algorithms for computing prefix averages - one with quadratic running time O(n^2) and one with linear running time O(n).
The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts such as algorithms, running time analysis, experimental vs theoretical analysis, pseudocode, primitive operations, counting operations, and asymptotic analysis using big-O notation. As an example, it analyzes an algorithm for finding the maximum element in an array, showing that it runs in O(n) time. It also analyzes two algorithms for computing prefix averages, showing one runs in O(n^2) time while the other improves it to O(n) time.
The document discusses algorithm analysis and asymptotic notation. It introduces algorithms for computing prefix averages of an array in quadratic and linear time. Specifically:
- An algorithm that computes prefix averages by directly applying the definition runs in O(n^2) time as its inner loop iterates over i elements n times.
- A more efficient algorithm that maintains a running sum runs in O(n) time, as each of its n iterations performs a constant number of operations.
- Asymptotic analysis allows algorithms to be classified based on growth rate, ignoring constant factors. This provides an algorithm-independent analysis of computational complexity.
Data Structure & Algorithms - Mathematicalbabuk110
This document discusses various mathematical notations and asymptotic analysis used for analyzing algorithms. It covers floor and ceiling functions, remainder function, summation symbol, factorial function, permutations, exponents, logarithms, Big-O, Big-Omega and Theta notations. It provides examples of calculating time complexity of insertion sort and bubble sort using asymptotic notations. It also discusses space complexity analysis and how to calculate the space required by an algorithm.
This document provides an overview of algorithms and asymptotic notation. It discusses that asymptotic notation allows algorithms to be compared based on how their running time grows relative to the input size. The key points covered include:
- Asymptotic notation describes the asymptotic behavior of a function, such as how fast algorithm running time grows relative to the input.
- Big O notation describes the worst case upper bound. If f(n) is O(g(n)), f(n) grows no faster than g(n).
- Common time complexities include O(1), O(log n), O(n), O(n log n), O(n^2).
- The dominating factor determines
This document provides an overview of algorithms including definitions, characteristics, design, and analysis. It defines an algorithm as a finite step-by-step procedure to solve a problem and discusses their key characteristics like input, definiteness, effectiveness, finiteness, and output. The document outlines the design of algorithms using pseudo-code and their analysis in terms of time and space complexity using asymptotic notations like Big O, Big Omega, and Big Theta. Examples are provided to illustrate linear search time complexity and the use of different notations to determine algorithm efficiency.
Performance analysis and randamized agorithamlilyMalar1
The document discusses performance analysis of algorithms in terms of space and time complexity. It provides examples to show how to calculate the space and time complexity of algorithms. Specifically, it analyzes the space and time complexity of a sum algorithm. For space complexity, it identifies the fixed and variable components, showing the space complexity is O(n). For time complexity, it analyzes the number of steps and their frequency to determine the time complexity is O(2n+3). The document also discusses other algorithm analysis topics like asymptotic notations, amortized analysis, and randomized algorithms.
The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts like best case, worst case, and average case running times. It explains that worst case analysis is most important and easiest to analyze. The document covers analyzing algorithms using pseudocode, counting primitive operations, and determining asymptotic running time using Big-O notation. Examples are provided to illustrate these concepts, including analyzing algorithms for finding the maximum element in an array and computing prefix averages.
Order notation is a mathematical method used to analyze algorithms as the problem size increases. It allows comparison of performance independent of machine-specific factors. Common notations include Big-O (upper bound), Big-Omega (lower bound), and Theta (tight bound). These describe the limiting behavior of execution time as the problem size approaches infinity and are used to classify algorithms by their running time growth rates like constant, logarithmic, linear, quadratic, and exponential.
Unit 1: Fundamentals of the Analysis of Algorithmic Efficiency, Units for Measuring Running Time, PROPERTIES OF AN ALGORITHM, Growth of Functions, Algorithm - Analysis, Asymptotic Notations, Recurrence Relation and problems
The document discusses stacks and queues as linear data structures. A stack follows LIFO (last in first out) where the last element inserted is the first removed. Common stack operations are push to insert and pop to remove elements. Stacks can be implemented using arrays or linked lists. A queue follows FIFO (first in first out) where the first element inserted is the first removed. Common queue operations are enqueue to insert and dequeue to remove elements. Queues can also be implemented using arrays or linked lists. Circular queues and priority queues are also introduced.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
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5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
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What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
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- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
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These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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Measuring algorithm performance
1. Measure Algorithm Performance
By: Asfaw Alene &Habitamu Asimare
Bahir Dar
Institute of Technology, BiT
Algorithm Analysis
Submitted to: Dr Million M.
06/20/2018
3. 1. Overview of Algorithm
An algorithm is a step by step procedure for solving a problem, based
on conducting a sequence of specified actions.
A computer program can be viewed as an elaborate algorithm. In
mathematics and computer science, an algorithm usually means a finite
procedure that solves a real world problem.
An algorithm can be used in
For search engines
Encryption technique
Memory management
Resource allocation (Operating Systems implementations)
4. Continued…
Basically Algorithm plays a great role to make computers system efficiently. The
following is the points to be remembered in Algorithm
An algorithm is a sequence of unambiguous instructions.
An algorithm is a well-defined procedure that allows a computer to solve a
problem
The algorithm is described as a series of logical steps in a language that is
easily understood
Algorithms is computer understandable actions that can be implemented in
Programming languages
In fact, it is difficult to think of a task performed by your computer that does
not use algorithms.
5. 2. Asymptotic Notations
Three asymptotic notations are mostly used to represent time
complexity of algorithms.
1. The theta(Θ) Notation
bounds a functions from above and below, so it defines exact asymptotic behavior.
A simple way to get Theta notation of an expression is to drop low order terms and ignore leading
constants.
For example, consider the following expression.
3n3 + 6n2 + 6000 = Θ(n3)
2. Big O Notation
defines an upper bound of an algorithm, it bounds a function only from above.
have to use two statements for best and worst cases in theta notations:
1. The worst case time complexity of Insertion Sort is Θ(n^2).
2. The best case time complexity of Insertion Sort is Θ(n).
In case of Big O notations we select worst case which is O(n^2)
3. Omega(Ω) Notation
Just as Big O notation provides an asymptotic upper bound on a function, Ω notation provides an
asymptotic lower bound
the Omega notation is the least used notation among all three.
6. 3. ANALYSIS
We can have three cases to analyze an algorithm:
Worst Case
In the worst case analysis, we calculate upper bound on running time of an algorithm.
We must know the case that causes maximum number of operations to be executed.
Average Case
n average case analysis, we take all possible inputs and calculate computing time for all of the
inputs.
Sum all the calculated values and divide the sum by total number of inputs. We must know (or
predict) distribution of cases.
Best Case
we calculate lower bound on running time of an algorithm. We must know the case that causes minimum
number of operations to be executed.
7. Asymptotic notations with examples
1 < logn < 𝑛 < n < nlogn< n < n2<n3 < ……. 2n < 3n < .. nn
Lower bound Upper bound
Asymptotic notations are mathematical tools to represent time complexity of
algorithms for asymptotic analysis.
The mostly used asymptotic notations
1. Big O Notation:
2. Θ Notation
3. Ω Notation
8. Big O-Notation (Upper limit )
For the function F(n) = O(n) , iff there exists positive constants c and n0 ,
such that f(n) ≤ c* g(n) for all n ≥ n0.
Example f(n)= 2n+3; what is the time complexity of F(n) ?
Solution chose the c value and g(n) value on the since O
notation is the upper bound so it is only possible to chose the
upper value. so we can chose c=10, and g(n)=n , for n>=1
• 2n+3 <10n is true
the time complexity of F(n) will be = O(n).
9. Omega Ω Notation (lower bound )
For the function F(n) = Ω( n) , iff there exists positive constants
C and n0 , such that f(n) ≥ c* g(n) for all n ≥ n0.
Example f(n)= 2n+3; what is the time complexity of F(n) ?
Solution chose the c value and g(n) value on the since Ω
notation is the upper bound so it is only possible to chose the
upper value. so we can chose c=1, and g(n)=n , for n>=1
2n+3 > n is true
the time complexity of F(n) will be = Ω (n).
10. Omega Ω Notation (lower bound )
For the function F(n) = Ω( n) , iff there exists positive constants
C and n0 , such that f(n) ≥ c* g(n) for all n ≥ n0.
Example f(n)= 2n+3; what is the time complexity of F(n) ?
Solution chose the c value and g(n) value on the since Ω
notation is the upper bound so it is only possible to chose the
upper value. so we can chose c=1, and g(n)=logn , for n>=1
2n+3 > logn is true
the time complexity of F(n) will be = Ω (logn).
11. Theta Θ-Notation (contd)
For the function F(n) = Θ(g(n)) , iff there exists positive constants c1, c2
and n0 , such that c1*gn <= f(n) ≤ c2* g(n) for all n ≥ n0. }
Example f(n)= 2n+3; what is the time complexity of F(n) ?
Solution chose the c1,c2 value and g(n) value on the since Θ
notation is the average bound so it is only possible to chose the
value which is the multiple of n. so we can chose c1=1, c2 = 5,
and g(n)=n , for n>=1
• 1n< 2n+1<5n is true, so
the time complexity of F(n) will be = Θ(n).
12. BEST CASE ANALYSIS (CONTD …)
Consider for the following example
A linear searching
From this example if we are searching a key element that are
present at first index is called best case
Best case time will be = 1, i.e B(n) or we can use notations
Big-O Notation and will be O(n)=1;
2
8 6 12 5 7 9 4 3 16
13. WORST CASE ANALYSIS
In the worst case analysis, we calculate upper bound on
running time of an algorithm. We must know the case that
causes maximum number of operations to be executed.
Consider for the following example
A linear searching worst case analysis
From this example if we are searching a key element that are
present at last of the index elements are called best case
2
8 6 12 5 7 9 4 3 16
14. AVERAGE CASE ANALYSIS
Consider for the following example
A linear searching average case analysis
From this example if we are searching a key element that are
present at middle of the index elements are called best case.
If we are looking from n elements will be
A(n)= (n+1)/2
What if you are given to analyze the case’s in Binary Search Tree ?
8 6 12 5 7 9 4 3 16
15. ADVANTAGE AND DISADVANTAGES
Advantages Disadvantages
1. To understand the complexity
that the program require
1. does not account for memory access times
1. to minimize unnecessary
operations
1. data size can determines the algorithm
behavior
1. solve real-world problem 1. sometimes the best and worst cases boundary
can’t algorithm performance
16. 4. Conclusion
Algorithms are the backbone of the working structure of computer
system.
Effectiveness and Correctness allows us to analyze the performance of
algorithm.
Beyond working on algorithm we have two think of two major aspects
of algorithm
Time :
Instructions take time.
How fast does the algorithm perform?
What affects its runtime?
Space :
Data structures take space
What kind of data structures can be used?
How does choice of data structure affect the runtime?
So space and time is challenges of algorithm performance
17. Continued…
Strength of measuring algorithm performance analysis
It expresses the amount of work for a given algorithm as being
proportional to a bounding function.
Weakness of measuring algorithm performance analysis
independent of implementation details such as the choice of computer
hardware
The asymptotic notation can be equal in some cases, like worst and best
cases.
SO that when the performance of algorithm is done, the analysis
should include what type of the resource should support while
implementing/coding the algorithm
18. 5. Reference
• Weiss, M.A., Data Structures and Problem Solving Using JAVA, Third Edition,
AddisonWesley Publishing Company, Inc., Reading, MA, 2006, pp. 313-331.
• Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to
algorithms (3rd ed). MIT Press
• Musselman, R. (2007). Robustness: A better measure of algorithm performance.
Thesis, Naval Postgraduate School, Monterey, CA.
• McMaster, K., Sambasivam, S., Rague, B., & Wolthuis, S. (2015). Distribution of
Execution Times for Sorting Algorithms Implemented in Java. Proceedings of
Informing Science & IT Education Conference (InSITE) 2015, 269- 283
• Mr. Rajinikanth B., What is Performance Analysis of an algorithm? , 2009,
http://btechsmartclass.com/DS/U1_T2.html last accessed on 17 June 2018.