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Smoothsort is an alternative sorting algorithm that works by maintaining a family of heaps called Leonardo heaps. It works by inserting elements into the appropriate Leonardo heap and then restoring the heap property more efficiently than standard heapsort. Smoothsort runs in O(n log n) time like heapsort but has a lower asymptotic graph, making it more efficient in practice. It works by inserting into the appropriate Leonardo heap based on its size and then fixing violations by swapping elements up the tree.

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Bottom up parser

Bottom-up parsing builds a derivation by working from the input sentence back toward the start symbol S. It is preferred in practice and also called LR parsing, where L means tokens are read left to right and R means it constructs a rightmost derivation. The two main types are operator-precedence parsing and LR parsing, which covers a wide range of grammars through techniques like SLR, LALR, and LR parsing. LR parsing reduces a string to the start symbol by inverting productions through identifying handles and replacing them.

Pipelining

Pipelining, Chapter 6, Computer Organization, Carl Hamacher Safwat Zaky Zvonko Vranesic, McGraw-Hill, 2002

Leftist heap

This document discusses leftist heaps, which are a type of priority queue implemented as a variant of a binary heap. Leftist heaps maintain the property that the right descendant of each node has a lower rank, or distance to the nearest leaf node, than the left descendant. This property helps keep the tree balanced during operations like insertion and merging of heaps that have a time complexity of O(log n). Deletion of the minimum element involves disconnecting and merging the left and right subtrees.

Heaps

A heap data structure is a binary tree that satisfies two properties: it is a complete binary tree where each level is filled from left to right, and the value stored at each node is greater than or equal to the values of its children (the heap property). Heaps can be implemented using arrays where the root is at index 0, left child at 2i+1, and right child at 2i+2. The basic heap operations like building a heap from an array and heapifying subtrees run in O(log n) time, allowing priority queues and other applications to be efficiently implemented using heaps.

Operator Precedence Grammar

This document discusses operator precedence parsing and describes the third and fourth steps:
- Parsing the input string based on a precedence relation table by shifting and reducing tokens.
- Constructing a parse tree from the parse table by focusing on reduce actions and building the tree from the bottom up.
Two examples are provided to demonstrate how operator precedence parsing can determine if an input string is valid based on a given grammar. Parse tables and trees are constructed to show the parsing process.

Introduction to Garbage Collection

Garbage collection is a form of automatic memory management used in computer programs to reclaim memory occupied by objects that are no longer needed. John McCarthy invented garbage collection for Lisp in 1959. Languages either use garbage collection or require manual memory management through techniques like allocating and freeing memory. Common garbage collection algorithms include reference counting, mark and sweep, and generational collection. The Java Virtual Machine uses different garbage collectors like serial, parallel, concurrent mark and sweep, and Garbage First collectors to reclaim memory in the Java heap.

Understanding Feature Space in Machine Learning

An explanation of fundamental concepts of features and models in machine learning, building on our geometric intuition of high dimensional spaces.

Tower of hanoi

The objective of the puzzle is to move the entire stack to another rod, obeying the following rules:
* Only one disk can be moved at a time.
* Each move consists of taking the upper disk from one of the rods and sliding it onto another rod, on top of the other disks that may already be present on that rod.
* No disk can be placed on top of a smaller disk than itself.

Bottom up parser

Bottom-up parsing builds a derivation by working from the input sentence back toward the start symbol S. It is preferred in practice and also called LR parsing, where L means tokens are read left to right and R means it constructs a rightmost derivation. The two main types are operator-precedence parsing and LR parsing, which covers a wide range of grammars through techniques like SLR, LALR, and LR parsing. LR parsing reduces a string to the start symbol by inverting productions through identifying handles and replacing them.

Pipelining

Pipelining, Chapter 6, Computer Organization, Carl Hamacher Safwat Zaky Zvonko Vranesic, McGraw-Hill, 2002

Leftist heap

This document discusses leftist heaps, which are a type of priority queue implemented as a variant of a binary heap. Leftist heaps maintain the property that the right descendant of each node has a lower rank, or distance to the nearest leaf node, than the left descendant. This property helps keep the tree balanced during operations like insertion and merging of heaps that have a time complexity of O(log n). Deletion of the minimum element involves disconnecting and merging the left and right subtrees.

Heaps

A heap data structure is a binary tree that satisfies two properties: it is a complete binary tree where each level is filled from left to right, and the value stored at each node is greater than or equal to the values of its children (the heap property). Heaps can be implemented using arrays where the root is at index 0, left child at 2i+1, and right child at 2i+2. The basic heap operations like building a heap from an array and heapifying subtrees run in O(log n) time, allowing priority queues and other applications to be efficiently implemented using heaps.

Operator Precedence Grammar

This document discusses operator precedence parsing and describes the third and fourth steps:
- Parsing the input string based on a precedence relation table by shifting and reducing tokens.
- Constructing a parse tree from the parse table by focusing on reduce actions and building the tree from the bottom up.
Two examples are provided to demonstrate how operator precedence parsing can determine if an input string is valid based on a given grammar. Parse tables and trees are constructed to show the parsing process.

Introduction to Garbage Collection

Garbage collection is a form of automatic memory management used in computer programs to reclaim memory occupied by objects that are no longer needed. John McCarthy invented garbage collection for Lisp in 1959. Languages either use garbage collection or require manual memory management through techniques like allocating and freeing memory. Common garbage collection algorithms include reference counting, mark and sweep, and generational collection. The Java Virtual Machine uses different garbage collectors like serial, parallel, concurrent mark and sweep, and Garbage First collectors to reclaim memory in the Java heap.

Understanding Feature Space in Machine Learning

An explanation of fundamental concepts of features and models in machine learning, building on our geometric intuition of high dimensional spaces.

Tower of hanoi

The objective of the puzzle is to move the entire stack to another rod, obeying the following rules:
* Only one disk can be moved at a time.
* Each move consists of taking the upper disk from one of the rods and sliding it onto another rod, on top of the other disks that may already be present on that rod.
* No disk can be placed on top of a smaller disk than itself.

Sorting algorithms

Sorting algorithms in C++
An introduction to sorting algorithm, with details on bubble sort and merge sort algorithms
Computer science principles course

Dynamic storage allocation techniques in Compiler design

Related on Dynamic storage allocation techniques in Compiler design. Two methods are explicit and implicit with examples. Prepared by SVIT student.

Dag representation of basic blocks

The document discusses constructing a directed acyclic graph (DAG) to represent the computation of values in a basic block of code. It describes how to build the DAG by processing each statement and creating nodes for operators and values. The DAG makes it possible to analyze the code block to optimize computations by removing duplicate subexpressions and determine which values are used inside and outside the block.

NN_02_Threshold_Logic_Units.pdf

A Threshold Logic Unit (TLU) is a basic processing unit with inputs, weights, and a threshold that determines its binary output. TLUs can represent some Boolean functions geometrically but are limited in their capabilities. Networks of multiple TLUs can overcome these limitations and represent any Boolean function by decomposing it into linearly separable components.

Computer Arithmetic

This document discusses various computer arithmetic operations including addition, subtraction, multiplication, and division for signed magnitude and two's complement data representations. It describes the Booth multiplication algorithm, array multipliers for performing multiplication using combinational circuits, and the division algorithm. It also covers detecting divide overflow conditions.

Análise de Algoritmos - Programação Dinâmica

Slides da disciplina de Análise de Algoritmos, ministrada pelo Prof. Marcelo H. Carvalho no curso de Pós-Graduação em Ciência da Computação, FACOM - UFMS.

Code generation in Compiler Design

The document discusses code generation and register allocation techniques. It begins by describing register allocation and assignment approaches and their advantages and disadvantages. It then discusses various issues in register allocation like which values reside in registers and which register. It describes register allocation as an NP-hard problem and common heuristics like graph coloring. The rest of the document discusses specific strategies for register allocation like global register allocation, usage counts, and register allocation via graph coloring and interference graphs. It also discusses techniques like register spilling when there are not enough registers.

Register transfer and micro operation

This document discusses register transfer language and micro-operations. It describes how registers store information and how register transfer language is used to define the transfer of data between registers using micro-operations like shift, clear and load. It also discusses how bus systems, memory transfers, and arithmetic logic shift units are used to perform these micro-operations and transfer data.

On First-Order Meta-Learning Algorithms

This document summarizes and analyzes first-order meta-learning algorithms. It discusses MAML, which approximates the MAML objective using only first-order information (FOMAML). FOMAML is equivalent to applying the last gradient to the initial parameters. Reptile is also analyzed, which simply averages the parameter updates. In expectation, the gradients of MAML, FOMAML and Reptile depend on the average gradient and average inner product of gradients. Experiments show similar performance between FOMAML and Reptile. The analysis suggests SGD may generalize well due to being an approximation of MAML.

Chapter 8 : Memory

This document discusses memory management techniques used in operating systems, including:
- Base and limit registers that define the logical address space and protect memory accesses.
- Address binding from source code to executable addresses at different stages.
- The memory management unit (MMU) that maps virtual to physical addresses using base/limit registers.
- Segmentation architecture that divides memory into logical segments like code, data, stack, heap.

Heap sort

The document discusses heap sort, which is a sorting algorithm that uses a heap data structure. It works in two phases: first, it transforms the input array into a max heap using the insert heap procedure; second, it repeatedly extracts the maximum element from the heap and places it at the end of the sorted array, reheapifying the remaining elements. The key steps are building the heap, processing the heap by removing the root element and allowing the heap to reorder, and doing this repeatedly until the array is fully sorted.

Cache coherence ppt

This document discusses cache coherence in single and multiprocessor systems. It provides techniques to avoid inconsistencies between cache and main memory including write-through, write-back, and instruction caching. For multiprocessors, it discusses issues with sharing writable data, process migration, and I/O activity. Software solutions involve compiler and OS management while hardware uses coherence protocols like snoopy and directory protocols.

Basic Computer Organization and Design

This document discusses the basic concepts of computer architecture and organization. It describes the components of a basic computer including the CPU, memory, and I/O. It explains instruction formats, addressing modes, and the operation of a hardwired control unit. The control unit uses a sequence counter, instruction register, and decoders to generate control signals for instruction fetch, decode, and execution cycles. Interrupts are handled through an interrupt service routine stored in memory.

Deadlock dbms

In this presentation all concept of deadlock is included. and it explain in less slides so easy to explain.
It is also useful for study.

Yzm 2116 Bölüm 9 - Heap Binary Tree

Tam İkili Ağaç
Eksiksiz İkili Ağaç
Dizi Kullanarak İkili Ağaç Gerçekleştirimi
Heap, Max Heap, Min Heap
Öncelik Kuyruğu
HeapSort

Merge sort

Merge sort is a sorting algorithm that works by dividing an array into two halves, recursively sorting the halves, and then merging the sorted halves into a single sorted array. The document provides details on how merge sort works, including pseudocode for the main, merge sort, and merging functions. It analyzes the time complexity of merge sort as O(n log n), making it more efficient than other basic sorts with O(n^2) time complexity like bubble, selection, and insertion sort.

Insertion and merge sort

Insertion sort is a simple sorting algorithm that works by building a sorted array from left to right by inserting each element into its sorted position. It is more efficient for smaller data sets but less efficient for larger data sets compared to other algorithms like merge sort. Merge sort works by dividing an array into halves, recursively sorting the halves, and then merging the sorted halves into a single sorted array.

Extendible hashing

Extendible Hashing Example
Extendible hashing solves bucket overflow by splitting the bucket into two and if necessary increasing the directory size. When the directory size increases it doubles its size a certain number of times.

Operating System Chapter 4 Multithreaded programming

A thread is the basic unit of CPU utilization within a process. Multithreaded processes provide benefits like increased responsiveness, resource sharing, and ability to utilize multiple processors. There are different models for how user threads created by libraries map to kernel threads managed by the OS kernel, including many-to-one, one-to-one, and many-to-many mappings. Popular thread libraries include Pthreads, Win32 threads, and Java threads which provide APIs for creating and managing threads.

Shift reduce parser

This document describes shift-reduce parsing. Shift-reduce parsing is a bottom-up parsing approach where the input string is collapsed by reducing parts of the string according to production rules until the start symbol is reached, as opposed to top-down parsing which expands symbols. It uses two main data structures: an input buffer and a stack. Initially, it puts the input string in the buffer and a start symbol on the stack. It then performs the basic operations of shift, which moves symbols from the buffer to the stack, and reduce, which replaces symbols on the stack according to production rules. It halts when the start symbol remains on the stack and the buffer is empty, indicating successful parsing.

Heapsort

Heapsort is an O(n log n) sorting algorithm that uses a heap data structure. It works by first turning the input array into a max heap, where the largest element is stored at the root. It then repeatedly removes the root element and replaces it with the last element of the heap, and sifts it down to maintain the heap property. This produces the sorted array from largest to smallest. The heapify and reheap operations each take O(log n) time, and are performed n times, resulting in an overall time complexity of O(n log n).

Heapsort

Heapsort is an O(n log n) sorting algorithm that uses a heap data structure. It works by first turning the input array into a max heap, where the largest element is stored at the root. It then repeatedly removes the root element and replaces it with the last element of the heap, and sifts the new root element down to maintain the heap property. This produces a sorted array from largest to smallest in O(n log n) time.

Sorting algorithms

Sorting algorithms in C++
An introduction to sorting algorithm, with details on bubble sort and merge sort algorithms
Computer science principles course

Dynamic storage allocation techniques in Compiler design

Related on Dynamic storage allocation techniques in Compiler design. Two methods are explicit and implicit with examples. Prepared by SVIT student.

Dag representation of basic blocks

The document discusses constructing a directed acyclic graph (DAG) to represent the computation of values in a basic block of code. It describes how to build the DAG by processing each statement and creating nodes for operators and values. The DAG makes it possible to analyze the code block to optimize computations by removing duplicate subexpressions and determine which values are used inside and outside the block.

NN_02_Threshold_Logic_Units.pdf

A Threshold Logic Unit (TLU) is a basic processing unit with inputs, weights, and a threshold that determines its binary output. TLUs can represent some Boolean functions geometrically but are limited in their capabilities. Networks of multiple TLUs can overcome these limitations and represent any Boolean function by decomposing it into linearly separable components.

Computer Arithmetic

This document discusses various computer arithmetic operations including addition, subtraction, multiplication, and division for signed magnitude and two's complement data representations. It describes the Booth multiplication algorithm, array multipliers for performing multiplication using combinational circuits, and the division algorithm. It also covers detecting divide overflow conditions.

Análise de Algoritmos - Programação Dinâmica

Slides da disciplina de Análise de Algoritmos, ministrada pelo Prof. Marcelo H. Carvalho no curso de Pós-Graduação em Ciência da Computação, FACOM - UFMS.

Code generation in Compiler Design

The document discusses code generation and register allocation techniques. It begins by describing register allocation and assignment approaches and their advantages and disadvantages. It then discusses various issues in register allocation like which values reside in registers and which register. It describes register allocation as an NP-hard problem and common heuristics like graph coloring. The rest of the document discusses specific strategies for register allocation like global register allocation, usage counts, and register allocation via graph coloring and interference graphs. It also discusses techniques like register spilling when there are not enough registers.

Register transfer and micro operation

This document discusses register transfer language and micro-operations. It describes how registers store information and how register transfer language is used to define the transfer of data between registers using micro-operations like shift, clear and load. It also discusses how bus systems, memory transfers, and arithmetic logic shift units are used to perform these micro-operations and transfer data.

On First-Order Meta-Learning Algorithms

This document summarizes and analyzes first-order meta-learning algorithms. It discusses MAML, which approximates the MAML objective using only first-order information (FOMAML). FOMAML is equivalent to applying the last gradient to the initial parameters. Reptile is also analyzed, which simply averages the parameter updates. In expectation, the gradients of MAML, FOMAML and Reptile depend on the average gradient and average inner product of gradients. Experiments show similar performance between FOMAML and Reptile. The analysis suggests SGD may generalize well due to being an approximation of MAML.

Chapter 8 : Memory

This document discusses memory management techniques used in operating systems, including:
- Base and limit registers that define the logical address space and protect memory accesses.
- Address binding from source code to executable addresses at different stages.
- The memory management unit (MMU) that maps virtual to physical addresses using base/limit registers.
- Segmentation architecture that divides memory into logical segments like code, data, stack, heap.

Heap sort

The document discusses heap sort, which is a sorting algorithm that uses a heap data structure. It works in two phases: first, it transforms the input array into a max heap using the insert heap procedure; second, it repeatedly extracts the maximum element from the heap and places it at the end of the sorted array, reheapifying the remaining elements. The key steps are building the heap, processing the heap by removing the root element and allowing the heap to reorder, and doing this repeatedly until the array is fully sorted.

Cache coherence ppt

This document discusses cache coherence in single and multiprocessor systems. It provides techniques to avoid inconsistencies between cache and main memory including write-through, write-back, and instruction caching. For multiprocessors, it discusses issues with sharing writable data, process migration, and I/O activity. Software solutions involve compiler and OS management while hardware uses coherence protocols like snoopy and directory protocols.

Basic Computer Organization and Design

This document discusses the basic concepts of computer architecture and organization. It describes the components of a basic computer including the CPU, memory, and I/O. It explains instruction formats, addressing modes, and the operation of a hardwired control unit. The control unit uses a sequence counter, instruction register, and decoders to generate control signals for instruction fetch, decode, and execution cycles. Interrupts are handled through an interrupt service routine stored in memory.

Deadlock dbms

In this presentation all concept of deadlock is included. and it explain in less slides so easy to explain.
It is also useful for study.

Yzm 2116 Bölüm 9 - Heap Binary Tree

Tam İkili Ağaç
Eksiksiz İkili Ağaç
Dizi Kullanarak İkili Ağaç Gerçekleştirimi
Heap, Max Heap, Min Heap
Öncelik Kuyruğu
HeapSort

Merge sort

Merge sort is a sorting algorithm that works by dividing an array into two halves, recursively sorting the halves, and then merging the sorted halves into a single sorted array. The document provides details on how merge sort works, including pseudocode for the main, merge sort, and merging functions. It analyzes the time complexity of merge sort as O(n log n), making it more efficient than other basic sorts with O(n^2) time complexity like bubble, selection, and insertion sort.

Insertion and merge sort

Insertion sort is a simple sorting algorithm that works by building a sorted array from left to right by inserting each element into its sorted position. It is more efficient for smaller data sets but less efficient for larger data sets compared to other algorithms like merge sort. Merge sort works by dividing an array into halves, recursively sorting the halves, and then merging the sorted halves into a single sorted array.

Extendible hashing

Extendible Hashing Example
Extendible hashing solves bucket overflow by splitting the bucket into two and if necessary increasing the directory size. When the directory size increases it doubles its size a certain number of times.

Operating System Chapter 4 Multithreaded programming

A thread is the basic unit of CPU utilization within a process. Multithreaded processes provide benefits like increased responsiveness, resource sharing, and ability to utilize multiple processors. There are different models for how user threads created by libraries map to kernel threads managed by the OS kernel, including many-to-one, one-to-one, and many-to-many mappings. Popular thread libraries include Pthreads, Win32 threads, and Java threads which provide APIs for creating and managing threads.

Shift reduce parser

This document describes shift-reduce parsing. Shift-reduce parsing is a bottom-up parsing approach where the input string is collapsed by reducing parts of the string according to production rules until the start symbol is reached, as opposed to top-down parsing which expands symbols. It uses two main data structures: an input buffer and a stack. Initially, it puts the input string in the buffer and a start symbol on the stack. It then performs the basic operations of shift, which moves symbols from the buffer to the stack, and reduce, which replaces symbols on the stack according to production rules. It halts when the start symbol remains on the stack and the buffer is empty, indicating successful parsing.

Sorting algorithms

Sorting algorithms

Dynamic storage allocation techniques in Compiler design

Dynamic storage allocation techniques in Compiler design

Dag representation of basic blocks

Dag representation of basic blocks

NN_02_Threshold_Logic_Units.pdf

NN_02_Threshold_Logic_Units.pdf

Computer Arithmetic

Computer Arithmetic

Análise de Algoritmos - Programação Dinâmica

Análise de Algoritmos - Programação Dinâmica

Code generation in Compiler Design

Code generation in Compiler Design

Register transfer and micro operation

Register transfer and micro operation

On First-Order Meta-Learning Algorithms

On First-Order Meta-Learning Algorithms

Chapter 8 : Memory

Chapter 8 : Memory

Heap sort

Heap sort

Cache coherence ppt

Cache coherence ppt

Basic Computer Organization and Design

Basic Computer Organization and Design

Deadlock dbms

Deadlock dbms

Yzm 2116 Bölüm 9 - Heap Binary Tree

Yzm 2116 Bölüm 9 - Heap Binary Tree

Merge sort

Merge sort

Insertion and merge sort

Insertion and merge sort

Extendible hashing

Extendible hashing

Operating System Chapter 4 Multithreaded programming

Operating System Chapter 4 Multithreaded programming

Shift reduce parser

Shift reduce parser

Heapsort

Heapsort is an O(n log n) sorting algorithm that uses a heap data structure. It works by first turning the input array into a max heap, where the largest element is stored at the root. It then repeatedly removes the root element and replaces it with the last element of the heap, and sifts it down to maintain the heap property. This produces the sorted array from largest to smallest. The heapify and reheap operations each take O(log n) time, and are performed n times, resulting in an overall time complexity of O(n log n).

Heapsort

Heapsort is an O(n log n) sorting algorithm that uses a heap data structure. It works by first turning the input array into a max heap, where the largest element is stored at the root. It then repeatedly removes the root element and replaces it with the last element of the heap, and sifts the new root element down to maintain the heap property. This produces a sorted array from largest to smallest in O(n log n) time.

Heap sort

Combines the better attributes of merge sort and insertion sort.
Like merge sort, but unlike insertion sort, running time is O(nlgn).
Like insertion sort, but unlike merge sort, sorts in place.

splaytree-171227043127.pptx NNNNNNNNNNNNNNNNNNNNNNN

Splay trees are a self-adjusting binary search tree data structure. They perform basic operations like insertion, lookup, and removal in O(log n) amortized time by splaying nodes up the tree after access. This has the effect of moving frequently accessed nodes closer to the root over time. The document discusses the differences between splay trees and AVL trees, how splaying is done through different rotation cases, and how amortized analysis shows splay trees have O(log n) performance on average across operations.

Splay tree

Algorithm and data structures topic is Splay Tree.
A full comparison between splay tree and AVL tree and learn to create splay tree with expamples

Avl tree

The document discusses algorithms for balancing binary search trees, specifically AVL trees. It defines AVL trees as binary search trees where the heights of the left and right subtrees of every node differ by at most one. It explains that AVL trees maintain logarithmic time for operations through rotations to rebalance the tree after insertions or deletions. The key operations of inserting and removing nodes from an AVL tree are described, including cases requiring single or double rotations to restore the balance property.

Introduction to Algorithms

This document discusses algorithms and their analysis. It covers linear search, binary search, sorting algorithms, and analyzing time complexity using Big O, Big Theta, and Big Omega notation. Binary search runs in logarithmic time O(log n) while linear search takes linear time O(n). The traveling salesperson problem takes exponential time O(n!) and is considered an inefficient algorithm.

Cs1311lecture23wdl

The document discusses algorithm complexity and Big O notation. It introduces Big O notation as a way to evaluate and compare algorithms based on how their runtime grows with increasing input size. The key points are: Big O gives the worst-case upper bound growth rate; dominant terms are kept and constants are dropped when determining Big O; sequential steps are added while embedded steps are multiplied; and this allows algorithms to be analyzed and compared based on their asymptotic complexity.

AVL Tree

An AVL tree is a self-balancing binary search tree that guarantees search, insertion, and deletion operations will take O(log n) time on average. It achieves this by ensuring the heights of the left and right subtrees of every node differ by at most one. When an insertion or deletion causes a height imbalance of two, rotations are performed to rebalance the tree.

Lecture 11.1 : heaps

This document discusses heaps and their implementation and applications. It begins by defining heaps as binary trees where the value of each parent node is less than its children, forming a min heap. It then discusses the key properties of heaps, including finding the minimum element in O(1) time. The document goes on to describe implementing a heap using an array and the basic operations of insertion, deletion, and heapsort in O(logn) time. It concludes by discussing the advantages of heaps for fast insertion and removal and their applications in priority queues and sorting.

Realtime Analytics

Christoph Koch is a professor of Computer Science at EPFL, specializing in data management. Until 2010, he was an Associate Professor in the Department of Computer Science at Cornell University. Previously to this, from 2005 to 2007, he was an Associate Professor of Computer Science at Saarland University. Earlier, he obtained his PhD in Artificial Intelligence from TU Vienna and CERN (2001), was a postdoctoral researcher at TU Vienna and the University of Edinburgh (2001-2003), and an assistant professor at TU Vienna (2003-2005). He has won Best Paper Awards at PODS 2002, ICALP 2005, and SIGMOD 2011, an Outrageous Ideas and Vision Paper Award at CIDR 2013, a Google Research Award (in 2009), and an ERC Grant (in 2011). He is a PI of the FET Flagship Human Brain Project and of NCCR MARVEL, a new Swiss national research center for materials research. He (co-)chaired the program committees of DBPL 2005, WebDB 2008, ICDE 2011, VLDB 2013, and was PC vice-chair of ICDE 2008 and ICDE 2009. He has served on the editorial board of ACM Transactions on Internet Technology and as Editor-in-Chief of PVLDB.

Avl tree detailed

The document discusses algorithms for balanced binary search trees, specifically AVL trees. It begins by explaining that AVL trees guarantee logarithmic time for operations by self-balancing after insertions and deletions to keep the height of the tree balanced. It then covers the properties of AVL trees, including that the difference between the heights of any node's left and right subtrees can be at most 1. The document also discusses the insertion, deletion, and rebalancing rotations performed on AVL trees to maintain their balanced structure.

Tree traversal techniques

In computer science, tree traversal (also known as tree search) is a form of graph traversal and refers to the process of visiting (checking and/or updating) each node in a tree data structure, exactly once. Such traversals are classified by the order in which the nodes are visited. The following algorithms are described for a binary tree, but they may be generalized to other trees as well.

Searching/Sorting algorithms

The document discusses various searching and sorting algorithms. It begins by defining search algorithms as methods for finding items within a collection. It then covers linear search, which has O(n) complexity, and binary search, which has O(log n) complexity but requires a sorted list. The document also discusses sorting algorithms like bubble sort, selection sort, and merge sort. Merge sort uses a divide-and-conquer approach and has O(n log n) complexity, making it one of the most efficient common sorting algorithms. Code implementations and complexity analyses are provided for many of the algorithms.

HeapSort

Al Mansour University College
Software Engineering Department
Lec: Wael Esam Matti
Edited by Tuqa Aqeel

3.7 heap sort

Heapsort is a sorting algorithm that uses a heap data structure. It has a worst-case runtime of O(n log n), which makes it generally slower than quicksort but more consistent. A heap is a balanced binary tree where each node's value is greater than or equal to its children's values. To sort an array using heapsort, the array is first converted to a max heap by sifting nodes up. Then the largest element is removed from the root and placed at the end, and the new root is sifted down to re-form the heap. This process repeats until the array is fully sorted in O(n log n) time.

Advanced data structures and implementation

This document provides an overview of several advanced data structures and their implementations, including: top-down splay trees, red-black trees, top-down red-black trees, deterministic skip lists, AA-trees, treaps, k-d trees, and pairing heaps. For each data structure, key properties and implementation techniques are summarized, such as rotation types for splay trees, balancing rules and operations for red-black trees, and insertion and search processes for skip lists. Code implementations are referenced for several of the data structures.

Avl tree ppt

1. The document discusses the AVL tree, a self-balancing binary search tree. It was the first such tree invented by G.M. Adelson-Velsky and E.M. Landis in 1962.
2. In an AVL tree, the heights of the two child subtrees of any node can differ by at most one. Rebalancing operations are performed when this balance property is violated upon insertion or deletion.
3. There are four types of rebalancing rotations used - single left, single right, double left-right, and double right-left rotations. These restore the balance property and result in a height-balanced tree.

Sienna 4 divideandconquer

Divide-and-conquer is an algorithm design technique that involves dividing a problem into smaller subproblems, solving the subproblems recursively, and combining the solutions. The document discusses several divide-and-conquer algorithms including mergesort, quicksort, and binary search. Mergesort divides an array in half, sorts each half, and then merges the halves. Quicksort picks a pivot element and partitions the array into elements less than and greater than the pivot. Both quicksort and mergesort have average-case time complexity of Θ(n log n).

Lecture9 syntax analysis_5

Bottom-up parsing builds parse trees starting from the leaves (terminals) and working towards the root (start symbol). It can handle a more general class of grammars than top-down parsing. Shift-reduce parsing splits the input string into two parts separated by a special character, with terminals on the right. It uses two actions: shift moves a terminal from right to left, and reduce applies a production to remove symbols from the left. Conflicts can occur when it is possible to shift or reduce, or to reduce in different ways. Handles mark points where reduction also allows deriving the start symbol.

Heapsort

Heapsort

Heapsort

Heapsort

Heap sort

Heap sort

splaytree-171227043127.pptx NNNNNNNNNNNNNNNNNNNNNNN

splaytree-171227043127.pptx NNNNNNNNNNNNNNNNNNNNNNN

Splay tree

Splay tree

Avl tree

Avl tree

Introduction to Algorithms

Introduction to Algorithms

Cs1311lecture23wdl

Cs1311lecture23wdl

AVL Tree

AVL Tree

Lecture 11.1 : heaps

Lecture 11.1 : heaps

Realtime Analytics

Realtime Analytics

Avl tree detailed

Avl tree detailed

Tree traversal techniques

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The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).

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This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.

BPSC-105 important questions for june term end exam

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(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
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Haunted Houses by H W Longfellow for class 10

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220711130088 Sumi Basak Virtual University EPC 3.pptx

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THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...

The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.

A Free 200-Page eBook ~ Brain and Mind Exercise.pptx

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Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰

KHUSWANT SINGH.pptx ALL YOU NEED TO KNOW ABOUT KHUSHWANT SINGH

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Skimbleshanks-The-Railway-Cat by T S Eliot

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Temple of Asclepius in Thrace. Excavation results

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BPSC-105 important questions for june term end exam

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Educational Technology in the Health Sciences

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CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx

Haunted Houses by H W Longfellow for class 10

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220711130088 Sumi Basak Virtual University EPC 3.pptx

Bossa N’ Roll Records by Ismael Vazquez.

Bossa N’ Roll Records by Ismael Vazquez.

spot a liar (Haiqa 146).pptx Technical writhing and presentation skills

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THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...

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- 1. Analysis of Algorithm SmoothSort Habib Ullah MS(CS)
- 2. Science of Computer Programming Volume 1, Issue 3, Pages 223–233 Smoothsort, an alternative for sorting in situ (Edsger Wybe) http://www.sciencedirect.com/science/article/pii/0167642382900168
- 3. Contents • What is Smoothsort • Heapsort Analysis • Idea: A family of heaps • Leonardo Numbers & Trees • Smoothsort operations • Algo & Runtime Analysis • Conclusion
- 4. Waybe
- 6. Why is Heapsort O(n lg n)?
- 7. Why is Heapsort O(n lg n)?
- 8. Why is Heapsort O(n lg n)?
- 9. Why is Heapsort O(n lg n)?
- 14. Building a Leonardo Heap Insert Operation
- 15. Insert Operation
- 45. DE queuing from a Leonardo Heap Remove Operation
- 82. Algo & Runtime Analysis
- 83. • Insert – If last two heaps are two consecutive leonardo numbers • Add new element as there root – Else if the rightmost is not of size 1 • New element becomes a new heap of size 1. This 1 is taken to be L(1) – Else • New element becomes a new heap of size 1. This 1 is taken to be L(0) • Restore – Set new element as "current" heap. – While there is a heap to the left of the current heap and its root is larger than the current root and both of its child heap roots • Swap(left-root with current).[Now current is that left root] – While the current heap has a size greater than 1 and either child heap of the current heap has a root node greater than the root of the current heap • Swap the greater child root with the current root. That child heap becomes the current heap. C1 (Log N) times (Log N) Log N
- 84. Dequeue – Remove topmost node of rightmost heap. – If it has no children, we're done. – Otherwise: – Fix up the left of the two heaps. – Then fix up the right of the two heaps. For N elements to dequeue running time is (N LogN) but this is asymptotically far less than Heap sort. C1 Log N
- 111. Conclusion • Compared to Heap Sort – Efficient in Best Case – Equal in Worst Case but the asymptotic graph is far less than Heap Sort