Introduction to Algorithms
3rd Edition
ISBN: 9780262033848
Author: Thomas H. Cormen, Ronald L. Rivest, Charles E. Leiserson, Clifford Stein
Publisher: MIT Press
expand_more
expand_more
format_list_bulleted
Concept explainers
Question
Chapter 16.1, Problem 3E
Program Plan Intro
To show that the approach of selecting the activity of least duration from among the activities compatible with previously selected activities doesnot work.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
. The vacuum cleaner problem is a well-known search problem for a vacuum cleaner robot that works on Artificial Intelligence. In this problem to clean up the whole area. In the superficial world, the vacuum cleaner agent has a location sensor and a dirt sensor to know where it is (room A, room B, or any n room) and whether the room is dirty. Also, the robot needs to avoid the obstacle (stool) and find a new path. A possible performance measure is to maximize the number of clean rooms over a certain period.
For this problem, four main conditions should be considered:
in(x,y) means a robot at (x,y)
dirt(x,y) means there is a dirt at(x,y)
facing(d) means the robot is facing direction d, where d ={north, east, south, west} • hit(s) means the robot hits the obstacle s
possible actions the robot can do - turn, forward, suck-dirt, avoid
a) Write possible rules that can be used to perform the room cleaning plan
b) Execute this plan…
Is it conceivable, via the use of a variety of strategies, that the overhead caused by NPT might be reduced?
parallel programming:
Prove that there cannot be a deadlock if resources are linearly ordered and processes always acquire them in order.
Chapter 16 Solutions
Introduction to Algorithms
Ch. 16.1 - Prob. 1ECh. 16.1 - Prob. 2ECh. 16.1 - Prob. 3ECh. 16.1 - Prob. 4ECh. 16.1 - Prob. 5ECh. 16.2 - Prob. 1ECh. 16.2 - Prob. 2ECh. 16.2 - Prob. 3ECh. 16.2 - Prob. 4ECh. 16.2 - Prob. 5E
Ch. 16.2 - Prob. 6ECh. 16.2 - Prob. 7ECh. 16.3 - Prob. 1ECh. 16.3 - Prob. 2ECh. 16.3 - Prob. 3ECh. 16.3 - Prob. 4ECh. 16.3 - Prob. 5ECh. 16.3 - Prob. 6ECh. 16.3 - Prob. 7ECh. 16.3 - Prob. 8ECh. 16.3 - Prob. 9ECh. 16.4 - Prob. 1ECh. 16.4 - Prob. 2ECh. 16.4 - Prob. 3ECh. 16.4 - Prob. 4ECh. 16.4 - Prob. 5ECh. 16.5 - Prob. 1ECh. 16.5 - Prob. 2ECh. 16 - Prob. 1PCh. 16 - Prob. 2PCh. 16 - Prob. 3PCh. 16 - Prob. 4PCh. 16 - Prob. 5P
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.Similar questions
- Consider a fully-connected artificial neural network with one hidden layer, i.e., a multilayer perceptron (MLP), which has 5 inputs, 3 neurons in the hidden layer, and 1 output neuron. The relation between the output y and the inputs x = [x1, . . . , x5] is given by y(x) = f (w, φ(x)), where φ(x) = [φ1(x), φ2(x), φ3(x)] 1. Draw the diagram that shows the inputs, nuerons, connections, correspond-ing weight parameters, and activation functions. 2. Explain the relation y(x) = f (w, φ(x)): write the explicit relation, explainthe role of functions f and φ(x), and state examples of functions.arrow_forwardAn unweighted graph G = (V, E) does not have weights associated with its edges asnoted. A simple strategy to find the maximal matching in a greedy way is to randomlyselect an edge in the graph, include it in the matching and remove all of its adjacentedges from the graph as in the steps below.1. Input: An unweighted graph G = (V, E)2. Output: A maximal matching M of G3. M ← Ø4. E ← E5. while E = Ø6. randomly select e ∈ E7. M ← M ∪ {e}8. E ← E \ {e∪ all adjacent edges to e}implement this algorithm in Pythonarrow_forwardSuppose we want to use UCS and the A* algorithm on the graph below to find the shortest path from node S to node G. Each node is labeled by a capital letter and the value of a heuristic function. Each edge is labeled by the cost to traverse that edge. Perform A*, UCS, and BFS on this graph. Indicate the f, g, and h values of each node for the A*. e.g., S = 0 + 6 = 6 (i.e. S = g(S) + h(S) = f(S)). Additionally, show how the priority queue changes with time. Show the order in which the nodes are visited for BFS and UCS. Show the path found by the A*, UCS, and BFS algorithms on the graph above. Make this example inadmissible by changing the heuristic value at one of the nodes. What node do you choose and what heuristic value do you assign? What would be the A* algorithm solution then.arrow_forward
- What is the notion of homotopic class and how does it relate to path optimization?arrow_forwardBy contrasting it with the longest-common-subsequence problem, create a dynamic programming approach for the bigger-is-smarter elephant problem. To accomplish this, the LCS issue must be expanded to include letter weights.arrow_forwardExtend Lamport's Algorithm for k-mutual exclusion problem, which allows at most k processes enter the critical section at the same time.arrow_forward
- in a graph G = (N,E,C), where N are nodes, E edges between nodes, and the weight of an edge e ∈ E is given by C(e), where C(e) > 1, for all e ∈ E. the heuristic h that counts the least amount of edges from an initial state to a goal state. now removing edges from the graph, while keeping the heuristic values unchanged. Is the heuristic still consistent?arrow_forwardThe final code for finding the shortest weighted path across a directed level graph and solving the event scheduling problem are almost similar.algorithm Schedule (E, d) (E, d) pre-cond: A set of events E = ej with start time sj, end time fj, worth wj, and distances dj, j between them make up an instance. Optimal valid schedule of events optSol has event en as its conclusion.arrow_forwardWe are given the following training examples in 2D ((-3,5), +), ((-4, –2), +), ((2,1), -), ((4,3),–) Use +1 to map positive (+) examples and -1 to map negative (-) examples. We want to apply the learning algorithm for training a perceptron using the above data with starting weights wo = w1 = w2 = 0 and learning rate 7 = 0.1. In each iteration process the training examples in the order given above. Complete at most 3 iterations over the above training examples. What are the weights at the end of each iteration? Are these weights finalarrow_forward
- The language must be in python. Neural Network Units weights of [-1.2, -1.1, 3.3, -2.1] two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examplesarrow_forwardThe learning problem is to find the unknown (functional) relationship hy between objects x X and targets y y based solely on a sample z = (x, y) = ((x1, Y1), ..., (xms Ym)) = (X × y)m of size meN drawn iid from an unknown distribution Pxy. If the output space Y contains a finite number Y of elements then the task is called a classification learning problem.arrow_forward(i) Describe Banker’s algorithm for deadlock avoidance with supporting example Consider a computer system with has four identical units of a resource R. There are three processes each with a maximum claim of two units of resource R. Processes can request these resources in anyway, that is, two in one shot or one by one. The system always satisfies a request a request for a resource if enough resources are available. If the process doesn’t request any other kind of resource, show that the system never deadlock Give a solution for the following synchronization problem using semaphores Producer- Consumer Problem Readers- Writers Problem List out the issues in preprocessor scheduling that causes performance degradation in multiprocessor systemsarrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
- Operations Research : Applications and AlgorithmsComputer ScienceISBN:9780534380588Author:Wayne L. WinstonPublisher:Brooks Cole
Operations Research : Applications and Algorithms
Computer Science
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Brooks Cole