The document summarizes various greedy algorithms and optimization problems that can be solved using greedy approaches. It discusses the greedy method, giving the definition that locally optimal decisions should lead to a globally optimal solution. Examples covered include picking numbers for largest sum, shortest paths, minimum spanning trees (using Kruskal's and Prim's algorithms), single-source shortest paths (using Dijkstra's algorithm), activity-on-edge networks, the knapsack problem, Huffman codes, and 2-way merging. Limitations of the greedy method are noted, such as how it does not always find the optimal solution for problems like shortest paths on a multi-stage graph.
The document discusses various optimization problems that can be solved using the greedy method. It begins by explaining that the greedy method involves making locally optimal choices at each step that combine to produce a globally optimal solution. Several examples are then provided to illustrate problems that can and cannot be solved with the greedy method. These include shortest path problems, minimum spanning trees, activity-on-edge networks, and Huffman coding. Specific greedy algorithms like Kruskal's algorithm, Prim's algorithm, and Dijkstra's algorithm are also covered. The document concludes by noting that the greedy method can only be applied to solve a small number of optimization problems.
This document discusses greedy algorithms and provides examples of their use. It begins by defining characteristics of greedy algorithms, such as making locally optimal choices that reduce a problem into smaller subproblems. The document then covers designing greedy algorithms, proving their optimality, and analyzing examples like the fractional knapsack problem and minimum spanning tree algorithms. Specific greedy algorithms covered in more depth include Kruskal's and Prim's minimum spanning tree algorithms and Huffman coding.
The document contains 16 multiple choice questions about algorithms, data structures, and graph theory. Each question has 4 possible answers and the correct answer is provided. The maximum number of comparisons needed to merge sorted sequences is 358, and depth first search on a graph represented with an adjacency matrix has a worst case time complexity of O(n^2).
This assignment discusses two algorithm design techniques - dynamic programming and decrease-and-conquer. It provides questions to design algorithms using these techniques for problems like rod cutting, shortest paths on a chessboard, insertion sort, and checking graph connectivity. Students must submit the assignment by the given deadline or face point deductions, and no presentations will be held after a certain date.
This document discusses algorithms for solving the all pairs shortest path problem. It introduces the all pair distance problem for unweighted graphs, which can be solved by multiplying the adjacency matrix. For weighted graphs, it describes the randomized Boolean product witness matrix algorithm, which reduces the all pairs shortest path problem to matrix multiplication. Deterministic algorithms like Floyd-Warshall are also discussed.
The document discusses various backtracking techniques including bounding functions, promising functions, and pruning to avoid exploring unnecessary paths. It provides examples of problems that can be solved using backtracking including n-queens, graph coloring, Hamiltonian circuits, sum-of-subsets, 0-1 knapsack. Search techniques for backtracking problems include depth-first search (DFS), breadth-first search (BFS), and best-first search combined with branch-and-bound pruning.
Prim's algorithm is used to find the minimum spanning tree of a connected, undirected graph. It works by continuously adding edges to a growing tree that connects vertices. The algorithm maintains two lists - a closed list of vertices already included in the minimum spanning tree, and a priority queue of open vertices. It starts with a single vertex in the closed list. Then it selects the lowest cost edge that connects an open vertex to a closed one, adds it to the tree and updates the lists. This process repeats until all vertices are in the closed list and connected by edges in the minimum spanning tree. The algorithm runs in O(E log V) time when using a binary heap priority queue.
The document contains exercises, hints, and solutions for analyzing algorithms from a textbook. It includes problems related to brute force algorithms, sorting algorithms like selection sort and bubble sort, and evaluating polynomials. The solutions analyze the time complexity of different algorithms, such as proving that a brute force polynomial evaluation algorithm is O(n^2) while a modified version is linear time. It also discusses whether sorting algorithms like selection sort and bubble sort preserve the original order of equal elements (i.e. whether they are stable).
The document discusses various optimization problems that can be solved using the greedy method. It begins by explaining that the greedy method involves making locally optimal choices at each step that combine to produce a globally optimal solution. Several examples are then provided to illustrate problems that can and cannot be solved with the greedy method. These include shortest path problems, minimum spanning trees, activity-on-edge networks, and Huffman coding. Specific greedy algorithms like Kruskal's algorithm, Prim's algorithm, and Dijkstra's algorithm are also covered. The document concludes by noting that the greedy method can only be applied to solve a small number of optimization problems.
This document discusses greedy algorithms and provides examples of their use. It begins by defining characteristics of greedy algorithms, such as making locally optimal choices that reduce a problem into smaller subproblems. The document then covers designing greedy algorithms, proving their optimality, and analyzing examples like the fractional knapsack problem and minimum spanning tree algorithms. Specific greedy algorithms covered in more depth include Kruskal's and Prim's minimum spanning tree algorithms and Huffman coding.
The document contains 16 multiple choice questions about algorithms, data structures, and graph theory. Each question has 4 possible answers and the correct answer is provided. The maximum number of comparisons needed to merge sorted sequences is 358, and depth first search on a graph represented with an adjacency matrix has a worst case time complexity of O(n^2).
This assignment discusses two algorithm design techniques - dynamic programming and decrease-and-conquer. It provides questions to design algorithms using these techniques for problems like rod cutting, shortest paths on a chessboard, insertion sort, and checking graph connectivity. Students must submit the assignment by the given deadline or face point deductions, and no presentations will be held after a certain date.
This document discusses algorithms for solving the all pairs shortest path problem. It introduces the all pair distance problem for unweighted graphs, which can be solved by multiplying the adjacency matrix. For weighted graphs, it describes the randomized Boolean product witness matrix algorithm, which reduces the all pairs shortest path problem to matrix multiplication. Deterministic algorithms like Floyd-Warshall are also discussed.
The document discusses various backtracking techniques including bounding functions, promising functions, and pruning to avoid exploring unnecessary paths. It provides examples of problems that can be solved using backtracking including n-queens, graph coloring, Hamiltonian circuits, sum-of-subsets, 0-1 knapsack. Search techniques for backtracking problems include depth-first search (DFS), breadth-first search (BFS), and best-first search combined with branch-and-bound pruning.
Prim's algorithm is used to find the minimum spanning tree of a connected, undirected graph. It works by continuously adding edges to a growing tree that connects vertices. The algorithm maintains two lists - a closed list of vertices already included in the minimum spanning tree, and a priority queue of open vertices. It starts with a single vertex in the closed list. Then it selects the lowest cost edge that connects an open vertex to a closed one, adds it to the tree and updates the lists. This process repeats until all vertices are in the closed list and connected by edges in the minimum spanning tree. The algorithm runs in O(E log V) time when using a binary heap priority queue.
The document contains exercises, hints, and solutions for analyzing algorithms from a textbook. It includes problems related to brute force algorithms, sorting algorithms like selection sort and bubble sort, and evaluating polynomials. The solutions analyze the time complexity of different algorithms, such as proving that a brute force polynomial evaluation algorithm is O(n^2) while a modified version is linear time. It also discusses whether sorting algorithms like selection sort and bubble sort preserve the original order of equal elements (i.e. whether they are stable).
This document discusses dynamic programming and algorithms for solving all-pair shortest path problems. It begins by explaining dynamic programming as an optimization technique that works bottom-up by solving subproblems once and storing their solutions, rather than recomputing them. It then presents Floyd's algorithm for finding shortest paths between all pairs of nodes in a graph. The algorithm iterates through nodes, updating the shortest path lengths between all pairs that include that node by exploring paths through it. Finally, it discusses solving multistage graph problems using forward and backward methods that work through the graph stages in different orders.
The document discusses algorithms for finding shortest paths between all pairs of vertices in a directed graph, including:
- Floyd-Warshall algorithm, which uses dynamic programming and matrix multiplication to compute the shortest paths matrix in O(V^3) time.
- Johnson's algorithm, which first reweights the graph to make all edge weights nonnegative, allowing it to use Dijkstra's algorithm repeatedly to solve the all-pairs shortest paths problem more efficiently for sparse graphs.
- Reweighting transforms the original graph in a way that preserves shortest path distances while ensuring nonnegative edge weights.
In [8] Liang and Bai have shown that the - 4 kC snake graph is an odd harmonious graph for each k ³ 1.
In this paper we generalize this result on cycles by showing that the - n kC snake with string 1,1,…,1 when
n º 0 (mod 4) are odd harmonious graph. Also we show that the - 4 kC snake with m-pendant edges for
each k,m ³ 1 , (for linear case and for general case). Moreover, we show that, all subdivision of 2 k mD -
snake are odd harmonious for each k,m ³ 1 . Finally we present some examples to illustrate the proposed
theories.
This document discusses using persistent homology to analyze the topological structure of proteins and relate it to protein compressibility. It summarizes that researchers modeled protein molecules as alpha filtrations to obtain multi-scale insight into their tunnel and cavity structures. The persistence diagrams of the alpha filtrations capture the sizes and robustness of these features in a compact way. The researchers found a clear linear correlation between their topological measure and experimentally determined protein compressibility values.
This document discusses randomized algorithms. It begins by listing different categories of algorithms, including randomized algorithms. Randomized algorithms introduce randomness into the algorithm to avoid worst-case behavior and find efficient approximate solutions. Quicksort is presented as an example randomized algorithm, where randomness improves its average runtime from quadratic to linear. The document also discusses the randomized closest pair algorithm and a randomized algorithm for primality testing. Both introduce randomness to improve efficiency compared to deterministic algorithms for the same problems.
The document discusses various algorithms including dynamic programming, Warshall's and Floyd's algorithms, backtracking, branch and bound, graph coloring, the n-queen problem, Hamiltonian cycles, and the sum of subsets problem. It provides examples and explanations of these algorithms, such as using dynamic programming to solve the 0-1 knapsack problem and backtracking to solve the n-queen problem by trying different placements of queens on a chessboard.
This document provides the solution to an algorithms assignment involving minimum spanning trees. It includes:
1) Representations of the graph as an adjacency matrix and list
2) Pseudocode for Prim's and Kruskal's algorithms for finding minimum spanning trees
3) Step-by-step examples of applying Prim's and Kruskal's algorithms to a graph representing connecting houses with cable
4) A comparison of the time complexities of Prim's and Kruskal's algorithms using Big-O and Big-Theta notation
This document discusses minimum spanning tree algorithms. A minimum spanning tree (MST) is a subgraph of a connected, undirected graph that connects all the vertices together using the minimum possible total edge weight. The document describes what a spanning tree and MST are, provides examples of applications of spanning trees such as network design, and summarizes two common greedy algorithms for finding an MST: Kruskal's algorithm and Prim's algorithm.
The branch-and-bound method is used to solve optimization problems by traversing a state space tree. It computes a bound at each node to determine if the node is promising. Better approaches traverse nodes breadth-first and choose the most promising node using a bounding heuristic. The traveling salesperson problem is solved using branch-and-bound by finding an initial tour, defining a bounding heuristic as the actual cost plus minimum remaining cost, and expanding promising nodes in best-first order until finding the minimal tour.
The document discusses greedy algorithms and their applications. It provides examples of problems that greedy algorithms can solve optimally, such as the change making problem and finding minimum spanning trees (MSTs). It also discusses problems where greedy algorithms provide approximations rather than optimal solutions, such as the traveling salesman problem. The document describes Prim's and Kruskal's algorithms for finding MSTs and Dijkstra's algorithm for solving single-source shortest path problems. It explains how these algorithms make locally optimal choices at each step in a greedy manner to build up global solutions.
Skiena algorithm 2007 lecture16 introduction to dynamic programmingzukun
This document summarizes a lecture on dynamic programming. It begins by introducing dynamic programming as a powerful tool for solving optimization problems on ordered items like strings. It then contrasts greedy algorithms, which make locally optimal choices, with dynamic programming, which systematically searches all possibilities while storing results. The document provides examples of computing Fibonacci numbers and binomial coefficients using dynamic programming by storing partial results rather than recomputing them. It outlines three key steps to applying dynamic programming: formulating a recurrence, bounding subproblems, and specifying an evaluation order.
This document contains summaries of solutions to various LeetCode problems in Java. It begins with a 3-sentence summary of the Rotate Array problem and its solutions, followed by shorter 1-sentence summaries of other problems and their solutions, including Evaluate Reverse Polish Notation, Longest Palindromic Substring, Word Break, and more. Dynamic programming and recursion are discussed as approaches for some of the problems.
- The document discusses estimating structured vector autoregressive (VAR) models from time series data.
- A VAR model of order d is defined as xt = A1xt-1 + ... + Adxt-d + εt, where xt is a p-dimensional time series, Ak are parameter matrices, and εt is noise.
- The document proposes regularizing the VAR model estimation problem to promote structured sparsity in the parameter matrices Ak. This involves transforming the model into a linear regression form and applying group lasso or fused lasso regularization.
This document provides an overview of a course on network optimization. It introduces the instructor and textbook. It summarizes the Koenigsberg bridge problem, which helped establish the field of graph theory. It discusses the mathematical definitions and terminology used in networks, such as nodes, arcs, paths, and cycles. It outlines three fundamental network flow problems: the shortest path problem, maximum flow problem, and minimum cost flow problem. It describes where network optimization is applied, such as transportation and manufacturing systems. It introduces the topic of computational complexity and how algorithms are analyzed.
The document discusses dynamic programming and provides examples of problems that can be solved using dynamic programming including unidirectional traveling salesman problem, coin change, longest common subsequence, and longest increasing subsequence. Source code is presented for solving these problems using dynamic programming including dynamic programming tables, tracing optimal solutions, and time complexity analysis. Various online judges are listed that contain sample problems relating to these dynamic programming techniques.
Dynamic programming is a technique for solving problems with overlapping subproblems and optimal substructure. It works by breaking problems down into smaller subproblems and storing the results in a table to avoid recomputing them. Examples where it can be applied include the knapsack problem, longest common subsequence, and computing Fibonacci numbers efficiently through bottom-up iteration rather than top-down recursion. The technique involves setting up recurrences relating larger instances to smaller ones, solving the smallest instances, and building up the full solution using the stored results.
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. It works by starting with a single vertex, and at each step adding the edge with the minimum weight that connects the growing spanning tree to another vertex not yet included in the tree, as long as this does not create a cycle. The time complexity of Prim's algorithm is O(V log V + E).
The overlap-save method is used to filter long input sequences by breaking them into overlapping blocks. Each block overlaps with the previous block by M-1 samples, where M is the length of the impulse response. Circular convolution is performed on each block after zero-padding the impulse response. The first M-1 samples of each filtered block are discarded due to aliasing. The remaining samples are concatenated to form the final output.
I am Carl D. I am a Digital Signal Processing System Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, DePaul University, Chicago. I have been helping students with their homework for the past 12 years. I solve assignments related to Digital Signal System Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Digital Signal Processing System Assignments.
This document discusses dynamic programming and algorithms for solving all-pair shortest path problems. It begins by defining dynamic programming as avoiding recalculating solutions by storing results in a table. It then describes Floyd's algorithm for finding shortest paths between all pairs of nodes in a graph. The algorithm iterates through nodes, calculating shortest paths that pass through each intermediate node. It takes O(n3) time for a graph with n nodes. Finally, it discusses the multistage graph problem and provides forward and backward algorithms to find the minimum cost path from source to destination in a multistage graph in O(V+E) time, where V and E are the numbers of vertices and edges.
Project management techniques allow projects to be planned, monitored, and controlled effectively. The document discusses key project management steps including:
1. Representing the project as a network diagram with nodes and branches to show task dependencies and durations.
2. Using the Critical Path Method (CPM) to calculate earliest and latest start/finish times to determine the critical path and project completion time.
3. Conducting sensitivity analysis using the Program Evaluation and Review Technique (PERT) which considers probabilistic activity times to estimate mean times and variances for predicting project completion probabilities.
This document discusses dynamic programming and algorithms for solving all-pair shortest path problems. It begins by explaining dynamic programming as an optimization technique that works bottom-up by solving subproblems once and storing their solutions, rather than recomputing them. It then presents Floyd's algorithm for finding shortest paths between all pairs of nodes in a graph. The algorithm iterates through nodes, updating the shortest path lengths between all pairs that include that node by exploring paths through it. Finally, it discusses solving multistage graph problems using forward and backward methods that work through the graph stages in different orders.
The document discusses algorithms for finding shortest paths between all pairs of vertices in a directed graph, including:
- Floyd-Warshall algorithm, which uses dynamic programming and matrix multiplication to compute the shortest paths matrix in O(V^3) time.
- Johnson's algorithm, which first reweights the graph to make all edge weights nonnegative, allowing it to use Dijkstra's algorithm repeatedly to solve the all-pairs shortest paths problem more efficiently for sparse graphs.
- Reweighting transforms the original graph in a way that preserves shortest path distances while ensuring nonnegative edge weights.
In [8] Liang and Bai have shown that the - 4 kC snake graph is an odd harmonious graph for each k ³ 1.
In this paper we generalize this result on cycles by showing that the - n kC snake with string 1,1,…,1 when
n º 0 (mod 4) are odd harmonious graph. Also we show that the - 4 kC snake with m-pendant edges for
each k,m ³ 1 , (for linear case and for general case). Moreover, we show that, all subdivision of 2 k mD -
snake are odd harmonious for each k,m ³ 1 . Finally we present some examples to illustrate the proposed
theories.
This document discusses using persistent homology to analyze the topological structure of proteins and relate it to protein compressibility. It summarizes that researchers modeled protein molecules as alpha filtrations to obtain multi-scale insight into their tunnel and cavity structures. The persistence diagrams of the alpha filtrations capture the sizes and robustness of these features in a compact way. The researchers found a clear linear correlation between their topological measure and experimentally determined protein compressibility values.
This document discusses randomized algorithms. It begins by listing different categories of algorithms, including randomized algorithms. Randomized algorithms introduce randomness into the algorithm to avoid worst-case behavior and find efficient approximate solutions. Quicksort is presented as an example randomized algorithm, where randomness improves its average runtime from quadratic to linear. The document also discusses the randomized closest pair algorithm and a randomized algorithm for primality testing. Both introduce randomness to improve efficiency compared to deterministic algorithms for the same problems.
The document discusses various algorithms including dynamic programming, Warshall's and Floyd's algorithms, backtracking, branch and bound, graph coloring, the n-queen problem, Hamiltonian cycles, and the sum of subsets problem. It provides examples and explanations of these algorithms, such as using dynamic programming to solve the 0-1 knapsack problem and backtracking to solve the n-queen problem by trying different placements of queens on a chessboard.
This document provides the solution to an algorithms assignment involving minimum spanning trees. It includes:
1) Representations of the graph as an adjacency matrix and list
2) Pseudocode for Prim's and Kruskal's algorithms for finding minimum spanning trees
3) Step-by-step examples of applying Prim's and Kruskal's algorithms to a graph representing connecting houses with cable
4) A comparison of the time complexities of Prim's and Kruskal's algorithms using Big-O and Big-Theta notation
This document discusses minimum spanning tree algorithms. A minimum spanning tree (MST) is a subgraph of a connected, undirected graph that connects all the vertices together using the minimum possible total edge weight. The document describes what a spanning tree and MST are, provides examples of applications of spanning trees such as network design, and summarizes two common greedy algorithms for finding an MST: Kruskal's algorithm and Prim's algorithm.
The branch-and-bound method is used to solve optimization problems by traversing a state space tree. It computes a bound at each node to determine if the node is promising. Better approaches traverse nodes breadth-first and choose the most promising node using a bounding heuristic. The traveling salesperson problem is solved using branch-and-bound by finding an initial tour, defining a bounding heuristic as the actual cost plus minimum remaining cost, and expanding promising nodes in best-first order until finding the minimal tour.
The document discusses greedy algorithms and their applications. It provides examples of problems that greedy algorithms can solve optimally, such as the change making problem and finding minimum spanning trees (MSTs). It also discusses problems where greedy algorithms provide approximations rather than optimal solutions, such as the traveling salesman problem. The document describes Prim's and Kruskal's algorithms for finding MSTs and Dijkstra's algorithm for solving single-source shortest path problems. It explains how these algorithms make locally optimal choices at each step in a greedy manner to build up global solutions.
Skiena algorithm 2007 lecture16 introduction to dynamic programmingzukun
This document summarizes a lecture on dynamic programming. It begins by introducing dynamic programming as a powerful tool for solving optimization problems on ordered items like strings. It then contrasts greedy algorithms, which make locally optimal choices, with dynamic programming, which systematically searches all possibilities while storing results. The document provides examples of computing Fibonacci numbers and binomial coefficients using dynamic programming by storing partial results rather than recomputing them. It outlines three key steps to applying dynamic programming: formulating a recurrence, bounding subproblems, and specifying an evaluation order.
This document contains summaries of solutions to various LeetCode problems in Java. It begins with a 3-sentence summary of the Rotate Array problem and its solutions, followed by shorter 1-sentence summaries of other problems and their solutions, including Evaluate Reverse Polish Notation, Longest Palindromic Substring, Word Break, and more. Dynamic programming and recursion are discussed as approaches for some of the problems.
- The document discusses estimating structured vector autoregressive (VAR) models from time series data.
- A VAR model of order d is defined as xt = A1xt-1 + ... + Adxt-d + εt, where xt is a p-dimensional time series, Ak are parameter matrices, and εt is noise.
- The document proposes regularizing the VAR model estimation problem to promote structured sparsity in the parameter matrices Ak. This involves transforming the model into a linear regression form and applying group lasso or fused lasso regularization.
This document provides an overview of a course on network optimization. It introduces the instructor and textbook. It summarizes the Koenigsberg bridge problem, which helped establish the field of graph theory. It discusses the mathematical definitions and terminology used in networks, such as nodes, arcs, paths, and cycles. It outlines three fundamental network flow problems: the shortest path problem, maximum flow problem, and minimum cost flow problem. It describes where network optimization is applied, such as transportation and manufacturing systems. It introduces the topic of computational complexity and how algorithms are analyzed.
The document discusses dynamic programming and provides examples of problems that can be solved using dynamic programming including unidirectional traveling salesman problem, coin change, longest common subsequence, and longest increasing subsequence. Source code is presented for solving these problems using dynamic programming including dynamic programming tables, tracing optimal solutions, and time complexity analysis. Various online judges are listed that contain sample problems relating to these dynamic programming techniques.
Dynamic programming is a technique for solving problems with overlapping subproblems and optimal substructure. It works by breaking problems down into smaller subproblems and storing the results in a table to avoid recomputing them. Examples where it can be applied include the knapsack problem, longest common subsequence, and computing Fibonacci numbers efficiently through bottom-up iteration rather than top-down recursion. The technique involves setting up recurrences relating larger instances to smaller ones, solving the smallest instances, and building up the full solution using the stored results.
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. It works by starting with a single vertex, and at each step adding the edge with the minimum weight that connects the growing spanning tree to another vertex not yet included in the tree, as long as this does not create a cycle. The time complexity of Prim's algorithm is O(V log V + E).
The overlap-save method is used to filter long input sequences by breaking them into overlapping blocks. Each block overlaps with the previous block by M-1 samples, where M is the length of the impulse response. Circular convolution is performed on each block after zero-padding the impulse response. The first M-1 samples of each filtered block are discarded due to aliasing. The remaining samples are concatenated to form the final output.
I am Carl D. I am a Digital Signal Processing System Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, DePaul University, Chicago. I have been helping students with their homework for the past 12 years. I solve assignments related to Digital Signal System Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Digital Signal Processing System Assignments.
This document discusses dynamic programming and algorithms for solving all-pair shortest path problems. It begins by defining dynamic programming as avoiding recalculating solutions by storing results in a table. It then describes Floyd's algorithm for finding shortest paths between all pairs of nodes in a graph. The algorithm iterates through nodes, calculating shortest paths that pass through each intermediate node. It takes O(n3) time for a graph with n nodes. Finally, it discusses the multistage graph problem and provides forward and backward algorithms to find the minimum cost path from source to destination in a multistage graph in O(V+E) time, where V and E are the numbers of vertices and edges.
Project management techniques allow projects to be planned, monitored, and controlled effectively. The document discusses key project management steps including:
1. Representing the project as a network diagram with nodes and branches to show task dependencies and durations.
2. Using the Critical Path Method (CPM) to calculate earliest and latest start/finish times to determine the critical path and project completion time.
3. Conducting sensitivity analysis using the Program Evaluation and Review Technique (PERT) which considers probabilistic activity times to estimate mean times and variances for predicting project completion probabilities.
Branch and bound is a state space search method that generates all children of a node before expanding any children. It associates a cost or profit with each node and uses a min or max heap to select the next node to expand. For the travelling salesman problem, it constructs a permutation tree representing all possible routes and uses lower bounds and reduced cost matrices at each node to prune the search space and find an optimal solution.
A New Deterministic RSA-Factoring AlgorithmJim Jimenez
This document proposes a new deterministic algorithm for factoring RSA numbers (n = p * q) and describes how it works. The algorithm uses schoolboy multiplication and counting/probability concepts to sequentially produce possible values for the prime factors p and q in a way that their product equals the original RSA number n. It has two main procedures: 1) A Producer procedure that sequentially generates values for the digits of p and q to match the first half of the digits in n. 2) An Eliminator procedure that eliminates combinations of p and q that do not match the second half of digits in n, leaving the correct factors. Pseudocode is provided to demonstrate how it works on a sample number. The document concludes by analyzing the running
1. The document discusses topics for the second part of a course, including project management, inventory, decision analysis, and queuing.
2. It provides details on the project management chapter, including topics, dates, and questions to be covered.
3. The critical path method (CPM) is described as a technique for determining the completion time of a project by using a network diagram and calculating earliest and latest start/finish times.
This document discusses project management techniques for solving project problems. It covers the following key points:
1. The Critical Path Method (CPM) is used to determine the completion time of a project by representing activities as a network and calculating the earliest and latest start/finish times.
2. The critical path is identified as the longest path of activities with zero slack time.
3. The Program Evaluation and Review Technique (PERT) extends CPM by using three time estimates for each activity to calculate expected durations and variances.
4. Sensitivity analysis, like PERT, accounts for uncertainty in activity durations to determine completion time distributions rather than single values.
This document discusses various greedy algorithms, including Dijkstra's algorithm for finding shortest paths in graphs. It provides details on Dijkstra's algorithm, including its greedy approach, proof of correctness, and efficient implementations using priority queues. It also discusses applications of shortest path algorithms and how the techniques extend to related problems involving closed semirings rather than just numbers.
This presentation is the full application of discrete mathematics throughout a course and includes Set Theory, Functions nd Sequences, Automata Theory, Grammars and algorithm building.
Hierarchical matrix techniques for maximum likelihood covariance estimationAlexander Litvinenko
1. We apply hierarchical matrix techniques (HLIB, hlibpro) to approximate huge covariance matrices. We are able to work with 250K-350K non-regular grid nodes.
2. We maximize a non-linear, non-convex Gaussian log-likelihood function to identify hyper-parameters of covariance.
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).
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).
This document outlines divide and conquer algorithms for linear space sequence alignment. It discusses MergeSort as an example divide and conquer algorithm, and describes using a divide and conquer approach to solve the longest common subsequence (LCS) problem. It explains how to find the "middle vertex" between the source and sink for the LCS problem by dividing the problem space in half at each step. The document also covers using block alignment and the Four Russians speedup technique to solve sequence alignment problems in sub-quadratic time.
dynamic programming complete by Mumtaz Ali (03154103173)Mumtaz Ali
The document discusses dynamic programming, including its meaning, definition, uses, techniques, and examples. Dynamic programming refers to breaking large problems down into smaller subproblems, solving each subproblem only once, and storing the results for future use. This avoids recomputing the same subproblems repeatedly. Examples covered include matrix chain multiplication, the Fibonacci sequence, and optimal substructure. The document provides details on formulating and solving dynamic programming problems through recursive definitions and storing results in tables.
Chapter 4 discusses greedy algorithms and their application to several optimization problems. It covers the general greedy method, including the subset and ordering paradigms. Specific problems covered include the knapsack problem, job sequencing with deadlines, and minimum cost spanning trees. Algorithms provided to solve these problems greedily include Prim's and Kruskal's algorithms for minimum cost spanning trees. The chapter also discusses the optimality of greedy approaches for several problems through proofs.
This document describes sets and operations on sets related to numbers on a roulette wheel. It defines six sets - A (red numbers), B (black numbers), C (green numbers), D (even numbers), E (odd numbers), and F (numbers 1-12). It provides the elements of each set based on a standard American roulette wheel. It then calculates the unions and intersections of these sets according to the given operations. Tables and diagrams are provided to represent the set operations and relationships.
Low-rank tensor methods for stochastic forward and inverse problemsAlexander Litvinenko
The document discusses low-rank tensor methods for solving partial differential equations (PDEs) with uncertain coefficients. It covers two parts: (1) using the stochastic Galerkin method to discretize an elliptic PDE with uncertain diffusion coefficient represented by tensors, and (2) computing quantities of interest like the maximum value from the tensor solution in a efficient way. Specifically, it describes representing the diffusion coefficient, forcing term, and solution of the discretized PDE using tensors, and computing the maximum value and corresponding indices by solving an eigenvalue problem involving the tensor solution.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
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).
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(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:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
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.
2. 4 -2
The greedy method
Suppose that a problem can be solved by
a sequence of decisions. The greedy
method has that each decision is locally
optimal. These locally optimal solutions
will finally add up to a globally optimal
solution.
Only a few optimization problems can be
solved by the greedy method.
3. 4 -3
An simple example
Problem: Pick k numbers out of n
numbers such that the sum of these k
numbers is the largest.
Algorithm:
FOR i = 1 to k
pick out the largest number and
delete this number from the input.
ENDFOR
4. 4 -4
Shortest paths on a special graph
Problem: Find a shortest path from v0
to v3
.
The greedy method can solve this problem.
The shortest path: 1 + 2 + 4 = 7.
5. 4 -5
Shortest paths on a multi-stage graph
Problem: Find a shortest path from v0
to v3
in
the multi-stage graph.
Greedy method: v0
v1,2
v2,1
v3
= 23
Optimal: v0
v1,1
v2,2
v3
= 7
The greedy method does not work.
6. 4 -6
Solution of the above problem
dmin(i,j): minimum distance between i and
j.
This problem can be solved by the
dynamic programming method.
dmin(v0,v3)=min
3+dmin(v1,1,v3)
1+dmin(v1,2,v3)
5+dmin(v1,3,v3)
7+dmin(v1,4,v3)
7. 4 -7
Minimum spanning trees
(MST)
It may be defined on Euclidean space
points or on a graph.
G = (V, E): weighted connected
undirected graph
Spanning tree : S = (V, T), T ⊆ E,
undirected tree
Minimum spanning tree(MST) : a
spanning tree with the smallest total
weight.
8. 4 -8
An example of MST
A graph and one of its minimum costs
spanning tree
9. 4 -9
Kruskal’s algorithm for
finding MST
Step 1: Sort all edges into increasing order.
Step 2: Add the next smallest weight edge to the
forest if it will not cause a cycle.
Step 3: Stop if n-1 edges. Otherwise, go to
Step2.
10. 4 -10
An example of Kruskal’s algorithm
Sort with respect to cost
Step-1
11. 4 -11
An example of Kruskal’s algorithm
Sort with respect to cost
12. 4 -12
The details for constructing MST
How do we check if a cycle is formed when a
new edge is added?
By the SET and UNION method.
A tree in the forest is used to represent a
SET.
If (u, v) ∈ E and u, v are in the same set, then
the addition of (u, v) will form a cycle.
If (u, v) ∈ E and u∈S1
, v∈S2
, then perform
UNION of S1
and S2
.
14. 4 -14
Prim’s algorithm for finding
MST
Step 1: x ∈ V, Let A = {x}, B = V - {x}.
Step 2: Select (u, v) ∈ E, u ∈ A, v ∈ B
such that (u, v) has the smallest weight
between A and B.
Step 3: Put (u, v) in the tree. A = A ∪ {v},
B = B - {v}
Step 4: If B = ∅, stop; otherwise, go to
Step 2.
Time complexity : O(n2
), n = |V|.
(see the example on the next page)
19. 4 -19
Can we use Dijkstra’s algorithm to find the
longest path from a starting vertex to an
ending vertex in an acyclic directed graph?
There are 3 possible ways to apply Dijkstra’s
algorithm:
Directly use “max” operations instead of “min”
operations.
Convert all positive weights to be negative. Then
find the shortest path.
Give a very large positive number M. If the weight
of an edge is w, now M-w is used to replace w.
Then find the shortest path.
All these 3 possible ways would not work!
The longest path problem
22. 4 -22
The earliest time
The earliest time of an activity, ai, can occur is the length
of the longest path from the start vertex v0 to ai’s start
vertex.
( Ex: the earliest time of activity a7 can occur is 7. )
We denote this time as early(i) for activity ai.
∴ early(6) = early(7) = 7.
V0
V1
V2
V3
V4
V6
V7
V8
V5
finish
a0 = 6
start
a1 = 4
a2 = 5
a4 = 1
a3 = 1
a5 = 2
a6 = 9
a7 = 7
a8 = 4
a10 = 4
a9 = 2
6/?
0/?
7/? 16/?
0/?
5/?
7/?
14/?7/?4/?
0/?
18
23. 4 -23
The latest time
The latest time, late(i), of activity, ai, is defined to be the
latest time the activity may start without increasing the
project duration.
Ex: early(5) = 5 & late(5) = 8; early(7) = 7 & late(7) = 7
V0
V1
V2
V3
V4
V6
V7
V8
V5
finish
a0 = 6
start
a1 = 4
a2 = 5
a4 = 1
a3 = 1
a5 = 2
a6 = 9
a7 = 7
a8 = 4
a10 = 4
a9 = 2
late(5) = 18 – 4 – 4 - 2 = 8
late(7) = 18 – 4 – 7 = 7
6/6
0/1
7/7 16/16
0/3
5/8
7/10
14/147/74/5
0/0
24. 4 -24
Critical activity
A critical activity is an activity for which
early(i) = late(i).
The difference between late(i) and early(i) is
a measure of how critical an activity is.
Calculation
of
Latest Times
Calculation
of
Earliest Times
Finding
Critical path(s)
To solve
AOE Problem
25. 4 -25
Calculation of Earliest Times
Let activity ai is represented by edge (u, v).
early (i) = earliest [u]
late (i) = latest [v] – duration of activity ai
We compute the times in two stages:
a forward stage and a backward stage.
The forward stage:
Step 1: earliest [0] = 0
Step 2: earliest [j] = max {earliest [i] + duration of (i, j)}
i is in P(j)
P(j) is the set of immediate predecessors of j.
26. 4 -26
The backward stage:
Step 1: latest[n-1] = earliest[n-1]
Step 2: latest [j] = min {latest [i] - duration of (j, i)}
i is in S(j)
S(j) is the set of vertices adjacent from vertex j.
latest[8] = earliest[8] = 18
latest[6] = min{earliest[8] - 2} = 16
latest[7] = min{earliest[8] - 4} = 14
latest[4] = min{earliest[6] – 9; earliest[7] – 7} = 7
latest[1] = min{earliest[4] - 1} = 6
latest[2] = min{earliest[4] - 1} = 6
latest[5] = min{earliest[7] - 4} = 10
latest[3] = min{earliest[5] - 2} = 8
latest[0] = min{earliest[1] – 6; earliest[2] – 4; earliest[3] – 5} = 0
Calculation of Latest Times
28. 4 -28
The longest path(critical path) problem
can be solved by the critical path
method(CPM) :
Step 1:Find a topological ordering.
Step 2: Find the critical path.
(see [Horiwitz 1998].)
CPM for the longest path
problem
29. 4 -29
The 2-way merging problem
# of comparisons required for the linear 2-
way merge algorithm is m1
+ m2
-1 where m1
and m2
are the lengths of the two sorted lists
respectively.
The problem: There are n sorted lists, each of
length mi
. What is the optimal sequence of
merging process to merge these n lists into
one sorted list ?
30. 4 -30
Extended Binary Tree Representing a 2-way
Merge
Extended binary trees
31. 4 -31
An example of 2-way merging
Example: 6 sorted lists with lengths 2,
3, 5, 7, 11 and 13.
32. 4 -32
Time complexity for
generating an optimal
extended binary
tree:O(n log n)
33. 4 -33
Huffman codes
In telecommunication, how do we represent a
set of messages, each with an access
frequency, by a sequence of 0’s and 1’s?
To minimize the transmission and decoding
costs, we may use short strings to represent
more frequently used messages.
This problem can by solved by using an
extended binary tree which is used in the 2-
way merging problem.
34. 4 -34
An example of Huffman algorithm
Symbols: A, B, C, D, E, F, G
freq. : 2, 3, 5, 8, 13, 15, 18
Huffman codes:
A: 10100 B: 10101 C: 1011
D: 100 E: 00 F: 01
G: 11
A Huffman code Tree
35. 4 -35
Chapter 4 Greedy method
Input(A[1…n])
Solution ←ψ
for i ← 1 to n do
X ← SELECT(A) ( 最好有一 data structure ,經 preprocessing 後可以很快的找
到 ( 包括 delete))
If FEASIBLE( solution, x)
then solution ← UNION( select, x)
endif
repeat
Output (solution)
特點
(1) 做一串 decision
(2) 每個 decision 只關心自己是不是 optimal 一部份與其它無關 ( 可以 local
check)
Note
(1) Local optimal 須是 global optimal
(2) 有時裡面隱含一個 sorting
36. 4 -36
Knapsack problem
Given positive integers P1, P2, …, Pn,
W1, W2, …, Wn and M.
Find X1, X2, … ,Xn, 0≦Xi≦1 such that
is maximized.
Subject to
∑=
n
1i
iiXP
∑=
≤
n
1i
ii MXW
38. 4 -38
Job Sequencing with Deadlines
Given n jobs. Associated with job I is an
integer deadline Di≧0. For any job I the profit
Pi is earned iff the job is completed by its
deadline. To complete a job, one has to
process the job on a machine for one unit of
time.
A feasible solution is a subset J of jobs such
that each job in the subset can be completed
by its deadline. We want to maximize the
∑∈Ji iP
40. 4 -40
Optimal Storage on Tapes
There are n programs that are to be stored
on a computer tape of length L. Associated
with each program i is a length Li.
Assume the tape is initially positioned at
the front. If the programs are stored in the
order I = i1, i2, …, in, the time tj needed to
retrieve program ij
tj =
∑=
j
1k
ik
L
41. 4 -41
Optimal Storage on Tapes
If all programs are retrieved equally often,
then the
mean retrieval time (MRT) =
This problem fits the ordering paradigm.
Minimizing the MRT is equivalent to
minimizing
d(I) =
∑=
n
1j
jt
n
1
∑∑= =
n
1j
j
1k
ik
L
42. 4 -42
Example
Let n = 3, (L1,L2,L3) = (5,10,3). 6 possible
orderings. The optimal is 3,1,2
Ordering I d(I)
1,2,3 5+5+10+5+10+3 = 38
1,3,2 5+5+3+5+3+10 = 31
2,1,3 10+10+5+10+5+3 = 43
2,3,1 10+10+3+10+3+5 = 41
3,1,2 3+3+5+3+5+10 = 29
3,2,1, 3+3+10+3+10+5 = 34