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5.4 randomized datastructures
This document discusses randomized data structures and algorithms. It begins by motivating randomized data structures as a way to transform average case runtimes into expected runtimes that are not dependent on specific inputs. It then provides examples of randomized data structures like treaps and randomized skip lists that provide efficient operations like insertion, deletion, and search in expected logarithmic time. It also discusses how randomization can be applied in algorithms like primality testing.
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2.4 mst prim &kruskal demo
The document discusses minimum spanning trees and two algorithms for finding them: Prim's algorithm and Kruskal's algorithm. Prim's algorithm works by growing a spanning tree from an initial node, always adding the lowest cost edge that connects to a node not yet in the tree. Kruskal's algorithm sorts the edges by cost and builds up a spanning tree by adding edges in order as long as they do not form cycles. Both algorithms find optimal minimum spanning trees for weighted, undirected graphs.
4.2 bst 02
This document discusses binary search trees (BSTs) and their use for dynamic sets. It covers BST operations like search, insert, find minimum/maximum, and successor/predecessor. It also discusses how BSTs can be used to sort in O(n log n) time by inserting elements in order and performing an inorder traversal, similar to quicksort. Maintaining a height of O(log n) for BSTs is discussed as an area for future improvement.
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5.4 randomized datastructures
This document discusses randomized data structures and algorithms. It begins by motivating randomized data structures as a way to transform average case runtimes into expected runtimes that are not dependent on specific inputs. It then provides examples of randomized data structures like treaps and randomized skip lists that provide efficient operations like insertion, deletion, and search in expected logarithmic time. It also discusses how randomization can be applied in algorithms like primality testing.
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2.4 mst prim &kruskal demo
The document discusses minimum spanning trees and two algorithms for finding them: Prim's algorithm and Kruskal's algorithm. Prim's algorithm works by growing a spanning tree from an initial node, always adding the lowest cost edge that connects to a node not yet in the tree. Kruskal's algorithm sorts the edges by cost and builds up a spanning tree by adding edges in order as long as they do not form cycles. Both algorithms find optimal minimum spanning trees for weighted, undirected graphs.
4.2 bst 02
This document discusses binary search trees (BSTs) and their use for dynamic sets. It covers BST operations like search, insert, find minimum/maximum, and successor/predecessor. It also discusses how BSTs can be used to sort in O(n log n) time by inserting elements in order and performing an inorder traversal, similar to quicksort. Maintaining a height of O(log n) for BSTs is discussed as an area for future improvement.
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1.9 b trees eg 03
This document describes operations on a B-tree including insertion, deletion, splitting and merging of nodes. It shows a B-tree initially containing 26 items in a balanced structure with all leaves at the same level. Keys are added to the leaf nodes, causing splits that promote keys up the tree and rebalance the structure. Deletion is demonstrated by borrowing or merging with neighboring nodes, or demoting and promoting keys when underflow occurs.
5.2 divede and conquer 03
This document discusses divide-and-conquer algorithms and their time complexities. It begins with examples of finding the maximum of a set and binary search. It then presents the general steps of a divide-and-conquer algorithm and analyzes time complexity. Several algorithms are discussed including quicksort, merge sort, 2D maxima finding, closest pair problem, convex hull problem, and matrix multiplication. Strategies like divide, conquer, and merge are used to solve problems recursively in fewer comparisons than brute force methods. Many algorithms have a time complexity of O(n log n).
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5.4 randamized algorithm
Skip lists are a data structure for implementing dictionaries. They consist of multiple sorted lists, with the top list containing all elements and lower lists being subsequences. Searching works by dropping down lists until finding the target element or determining it is absent. Insertion and deletion use a randomized algorithm adding/removing elements from the appropriate lists. Analysis shows the expected space is O(n) and search, insertion and deletion times are O(log n), with these bounds also holding with high probability. Skip lists provide fast, simple dictionary implementation in practice.
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4.2 bst 03
1) Tree data structures involve nodes that can have zero or more child nodes and at most one parent node. Binary trees restrict nodes to having zero, one, or two children.
2) Binary search trees have the property that all left descendants of a node are less than the node's value and all right descendants are greater. This property allows efficient searches, inserts, and deletes that take O(log n) time on average.
3) Trees can become unbalanced over many insertions and deletions, affecting performance of operations. Various self-balancing binary search tree data structures use tree rotations to maintain balance.
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2.4 mst kruskal’s
Kruskal's algorithm is used to find the minimum spanning tree of a connected, undirected graph. It works by sorting the edges by weight and building up the minimum spanning tree by adding edges one by one if they do not form cycles, until n-1 edges are added where n is the number of nodes. The algorithm takes as input a weighted, connected graph and outputs the minimum spanning forest by iteratively selecting the lowest cost edge that avoids cycles.
4.4 hashing02
The document outlines various data structures and algorithms for implementing dictionaries and hash tables, including:
- Separate chaining, which handles collisions by storing elements that hash to the same value in a linked list. Find, insert, and delete take average time of O(1).
- Open addressing techniques like linear probing and quadratic probing, which handle collisions by probing to alternate locations until an empty slot is found. These have faster search but slower inserts and deletes.
- Double hashing, which uses a second hash function to determine probe distances when collisions occur, reducing clustering compared to linear probing.
5.3 dyn algo-i
This document summarizes a talk on dynamic graph algorithms. It begins with an introduction to dynamic graph algorithms, which involve maintaining a graph structure and answering queries efficiently as the graph undergoes a sequence of edge insertions and deletions. It then discusses several examples of fully dynamic algorithms for problems like connectivity, minimum spanning trees, and graph spanners. A key data structure introduced is the Euler tour tree, which represents a dynamic tree as a one-dimensional structure to support efficient updates and queries. The document concludes by outlining a fully dynamic randomized algorithm for maintaining connectivity under edge updates with polylogarithmic update time, using a hierarchical approach with multiple levels of edge partitions and ET trees.
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5.1 greedyyy 02
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2.6 all pairsshortestpath
The document discusses shortest path algorithms for weighted graphs. It introduces Dijkstra's algorithm and the Bellman-Ford algorithm for finding shortest paths. Dijkstra's algorithm works for graphs with non-negative edge weights, while Bellman-Ford can handle graphs with negative edge weights. The document also describes how to find shortest paths in directed acyclic graphs and compute all-pairs shortest paths.
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This document describes operations on a B-tree including insertion, deletion, splitting and merging of nodes. It shows a B-tree initially containing 26 items in a balanced structure with all leaves at the same level. Keys are added to the leaf nodes, causing splits that promote keys up the tree and rebalance the structure. Deletion is demonstrated by borrowing or merging with neighboring nodes, or demoting and promoting keys when underflow occurs.
5.2 divede and conquer 03
This document discusses divide-and-conquer algorithms and their time complexities. It begins with examples of finding the maximum of a set and binary search. It then presents the general steps of a divide-and-conquer algorithm and analyzes time complexity. Several algorithms are discussed including quicksort, merge sort, 2D maxima finding, closest pair problem, convex hull problem, and matrix multiplication. Strategies like divide, conquer, and merge are used to solve problems recursively in fewer comparisons than brute force methods. Many algorithms have a time complexity of O(n log n).
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5.4 randamized algorithm
Skip lists are a data structure for implementing dictionaries. They consist of multiple sorted lists, with the top list containing all elements and lower lists being subsequences. Searching works by dropping down lists until finding the target element or determining it is absent. Insertion and deletion use a randomized algorithm adding/removing elements from the appropriate lists. Analysis shows the expected space is O(n) and search, insertion and deletion times are O(log n), with these bounds also holding with high probability. Skip lists provide fast, simple dictionary implementation in practice.
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4.2 bst 03
1) Tree data structures involve nodes that can have zero or more child nodes and at most one parent node. Binary trees restrict nodes to having zero, one, or two children.
2) Binary search trees have the property that all left descendants of a node are less than the node's value and all right descendants are greater. This property allows efficient searches, inserts, and deletes that take O(log n) time on average.
3) Trees can become unbalanced over many insertions and deletions, affecting performance of operations. Various self-balancing binary search tree data structures use tree rotations to maintain balance.
RESUME-ARITRA BHOWMIK
Aritra Bhowmik is an electrical design engineer with over 4 years of experience in lighting, power distribution, and ELV system design for various industrial projects in India and abroad. He has expertise in lighting calculations, single line diagrams, cable sizing, earthing design, and 3D modeling. Currently working as an engineer at Larsen & Toubro Technology Services in Chennai, his responsibilities include design, detailing, coordination, and business continuity management. He holds a B.Tech in electrical engineering and is proficient in AutoCAD, DIALux, Revit MEP, and Microsoft Office applications.
2.4 mst kruskal’s
Kruskal's algorithm is used to find the minimum spanning tree of a connected, undirected graph. It works by sorting the edges by weight and building up the minimum spanning tree by adding edges one by one if they do not form cycles, until n-1 edges are added where n is the number of nodes. The algorithm takes as input a weighted, connected graph and outputs the minimum spanning forest by iteratively selecting the lowest cost edge that avoids cycles.
4.4 hashing02
The document outlines various data structures and algorithms for implementing dictionaries and hash tables, including:
- Separate chaining, which handles collisions by storing elements that hash to the same value in a linked list. Find, insert, and delete take average time of O(1).
- Open addressing techniques like linear probing and quadratic probing, which handle collisions by probing to alternate locations until an empty slot is found. These have faster search but slower inserts and deletes.
- Double hashing, which uses a second hash function to determine probe distances when collisions occur, reducing clustering compared to linear probing.
5.3 dyn algo-i
This document summarizes a talk on dynamic graph algorithms. It begins with an introduction to dynamic graph algorithms, which involve maintaining a graph structure and answering queries efficiently as the graph undergoes a sequence of edge insertions and deletions. It then discusses several examples of fully dynamic algorithms for problems like connectivity, minimum spanning trees, and graph spanners. A key data structure introduced is the Euler tour tree, which represents a dynamic tree as a one-dimensional structure to support efficient updates and queries. The document concludes by outlining a fully dynamic randomized algorithm for maintaining connectivity under edge updates with polylogarithmic update time, using a hierarchical approach with multiple levels of edge partitions and ET trees.
РЕКЛАМНЫЕ МОДЕЛИ
AIDA FCB models marketing and advertising techniques. AIDA represents Attention, Interest, Desire, and Action, the stages a consumer progresses through when encountering a product. FCB represents a model of motivation proposed by Foote, Cone, and Belding that identifies three domains that influence behavior: Feelings, Cognition, and Behavior.
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1) The document discusses techniques for mining data from the World Wide Web, including identifying authoritative web pages through link analysis algorithms like HITS and PageRank, mining multimedia data and web images through associated text and links, automatically classifying web documents, and analyzing web server logs to discover user access patterns through web usage mining.
2) It describes how web pages can be partitioned into semantic blocks and how block-level link analysis can be used to identify related images and organize them, as well as reduce noise in automatic web document classification.
3) Methods of web usage mining discussed include cleaning, condensing, and transforming log data to generate multidimensional views of user access patterns that can help discover customers, markets
Madhumita Project Coordinator/Business Analyst
This document provides a professional summary for Madhumita Bairagi, including her contact information, work experience, tools experience, education, and other relevant details. She has over 6 years of experience as a Project Coordinator and Business Analyst, facilitating daily stand-ups and sprint planning. She is skilled in requirements gathering, documentation, and coordinating projects between clients, development teams, and other stakeholders using tools like JIRA, ServiceNow, and Microsoft products. Her experience includes roles in project planning, testing, and release management for clients in insurance, healthcare, and pharmaceutical industries.
5.1 greedyyy 02
The document discusses greedy algorithms and provides examples of problems that can be solved using greedy techniques. It introduces the coin changing problem and activity selection problem. For activity selection, it demonstrates that a greedy approach of always selecting the activity with the earliest finish time results in an optimal solution. It provides pseudo-code for a greedy algorithm and proves that the greedy solution is optimal for the activity selection problem by showing there is always an optimal solution that makes the greedy choice and combining the greedy choice with the optimal solution to the remaining subproblem yields an optimal solution to the original problem.
2.6 all pairsshortestpath
The document discusses shortest path algorithms for weighted graphs. It introduces Dijkstra's algorithm and the Bellman-Ford algorithm for finding shortest paths. Dijkstra's algorithm works for graphs with non-negative edge weights, while Bellman-Ford can handle graphs with negative edge weights. The document also describes how to find shortest paths in directed acyclic graphs and compute all-pairs shortest paths.
Madhumita Project Coordinator/Business Analyst
This document provides a professional summary for Madhumita Bairagi, including her contact information, tools experience, and professional experience as a Project Coordinator/Business Analyst. She has over 6 years of experience facilitating Agile and Waterfall projects. Her roles have included requirement gathering, documentation, release management, and acting as a liaison between clients, development teams, and other stakeholders. She is proficient in tools like JIRA, ServiceNow, and Microsoft products. Her education includes a Bachelor's degree in Computer Science.
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160607 14 sw교육_강의안
- 3. 미래사회 미래교육 Future Society Video (online) : http://bit.ly/tfhnbox Future Society Video (for offline) : bit.ly/swebox Future Society Material Hub : http://bit.ly/tfhnhub
- 4. SW교육 목적 1. 국가경쟁 산업 : 김진형 소장님 (자료, 영상1, 2) 2. 미래역량 교육 : 김현철 교수님 (자료, 영상1, 2, 요약) 3. 개인 삶 : 신기헌 디자이너 (자료, 영상, 요약) 4. 난 : 1+2+3+@
- 5. 저는..
- 6. SW교육 목표 추상화 + 자동화
- 7. 여러분은..
- 10. Let’s Game! 일단 본 후 1. 눈치 게임 2. 이웃사랑 게임 3. 송광호(접어) 게임
- 11. SW교육 방법
- 12. SW교수학습 모형 goo.gl/GqEF10 1. 시연 중심 : 시연 / 모방 / 제작 2. 재구성 중심 : 놀이 / 수정 / 재구성 3. 개발 중심 : 탐구 / 설계 / 개발 4. 디자인 중심 : 요구분석 / 디자인 / 구현 / 공유 (피드백) 5. CT 중심 : 분해 / 패턴 인식 / 추상화 / 알고리즘 / 프로그래밍
- 14. 교육 사례 ● 문화 조성 ● 저스트 코딩 ● 선도부 / SW아카데미
- 15. 교과융합 SW교육 사례 ● EPL 요소 (순차, 반복, 조건, 함수) - goo.gl/rwFE8i ● 영어 ○ 비봇 - 알파벳, 방향 수행평가 https://youtu.be/DnDBdpJRPFE , https://youtu.be/-nkCpkFrAiA ○ 엔트리 - 공개수업 외 https://youtu.be/Ynq1jQB8EkQ?t=15m26s ○ 스크래치 - 공개수업 외 https://youtu.be/Epo4shUyAxw?t=34m17s ○ 마인크래프트 - 단어 쓰기 https://youtu.be/qc4Soa0_7ZU ○ 코스페이시스 - 사물의 위치 https://youtu.be/_204uyobACY ● 과학 ○ Jr스크래치 - 물의상태변화 https://youtu.be/TxuccZRmriY ○ 코듀 - 화산 만들기 https://youtu.be/C4uz7JM90Kw ○ 엔트리 - 지진대비 ● 창체 - 퍼플렉서스, 라이트봇, The Poos
- 16. 배영형 언플러그드SW수업 제안 ● 비봇 ● 퍼플렉서스 ● 루비 보드 게임 (2015유럽코딩주간) ● 창체 언플러그드 활동 (10선) bit.ly/upchoiman 눈치게임, 이웃사랑, 라인디자인, 하노이탑, 윷놀이, 페그퍼즐, 댄스, 종이접기, 헬로우 굿바이놀이 ● 결국 교사/학생/교재의 결에 맞게
- 17. Let’s CodyRoby! 최만드림 SW교육 공개폴더(bit.ly/sw_BSC) 내 유럽코딩 보드게임 https://youtu.be/D5hQ9UTDQ6s