1. In the activity selection problem, consider a greedy approach which repeatedly selects the last activity to start (of those which are compatible with all previously selected activities) instead of the first activity to finish. Describe how this approach is a greedy algorithm, and show that it yields an optimal solution. You may choose to either (1) show this directly or (2) to show that it is equivalent to the "first activity to start" approach which you can cite as proven based on lecture and the textbook. (This is similar to problem 15.1-2 from CLRS)

1. In the activity selection problem, consider a greedy approach which repeatedly selects the last activity to start (of those which are compatible with all previously selected activities) instead of the first activity to finish. Describe how this approach is a greedy algorithm, and show that it yields an optimal solution. You may choose to either (1) show this directly or (2) to show that it is equivalent to the "first activity to start" approach which you can cite as proven based on lecture and the textbook. (This is similar to problem 15.1-2 from CLRS)

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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I need to implement a python code for Q learning and SARSA method. My example involves a cliff walking experiment where the rewards are -1 except for the region marked as cliff if the agent steps there the reward is -100 and the agent is sent back to the start. The values used are alpha = 0.1, y or gamma = 1 and the e- greedy action is 0.1. After using these values on both algorithm the results needs to be shown by using matplot. The graph should have episodes in the x axis and sum of rewards during episode on the y axis. It needs to be smoothed out by averaging over 10 runs and, plotting moving average over last 10 episodes. This needs to be done in python using numpy and panda but I'm not allowed to use gym library or any other library.
In the context of evolutionary computing the goal function is known as the fitnessfunction and the problem is to maximize it. The typical formulation has to be changedin a simple way.min f (x) = − max[− f (x)] (4.9)Another requirement is that the goal function is positive.Phenotype evolution treats x as a phenotype and the goal function as the fitnessfunction. The typical framework for the method is as follows:
Consider navigating the maze shown below (same maze used in the homework). (N M 4 2 K 2 2 2 E B G 2 2 F H 2 The maze is represented as a graph with edge costs as shown on the edges. The edge cost is 1 for all edges where the cost is not shown. Let B be the initial state and G is the goal state. The heuristic cost of every node to reach G is : h(n) Th A 5.1 B 4.1 C 3.9 D 14 E 2.2 IF 3.8 G 0 IH 3.7 IJ 7 IK 16 L 14 IM 0.5 IN 1.5 IP 1.8 S 4.5 Show the steps of an A* search starting from B to goal G: . Show the frontier and explored set in every iteration (i.e., complete the above table) List the vertices in the order they are expanded.
Hi please answer the following follow up questions as well, posted them as another question. Question 4 For the 9-tile soring problem, assume that you start from this initial state 7 2 4 5 6 8 3 1 The Goal State is: 1 2 3 4 5 6 7 8 The cost of moving any tile is 1. Let the heuristic function h(n) = number of misplaced tiles. For the shown configuration, there are four options for the next move: Move 5 to the right Move 6 to the left Move 2 down Move 3 up Each of these moves has a value f(n) = h(n) + g(n). If we choose to Move 5 to the right, then g(n) = 1. That is, it took us one step to reach this state from the initial state. h(n) = number of misplaced tiles. The misplaced tiles are {7,4,8,3,1}. So the number of misplaced tiles = h(n) = 5. If we choose to Move 6 to the left, g(n) is still = 1, but h(n) will change because the number of misplaced tiles is different. A* works by computing f(n) = h(n) + g(n) for each of these possible moves. Then it…
Write and implement the best first search algorithm where we have given the Start state and Goal state and we want to reach the goal state. 4 7 3 1 8 2 5 6 Initial state 1 2 3 8 4 7 6 5 Goal state
Correct answer will be upvoted else downvoted. There are n focuses on an endless plane. The I-th point has facilitates (xi,yi) to such an extent that xi>0 and yi>0. The directions are not really integer. In one maneuver you play out the accompanying activities: pick two focuses an and b (a≠b); move point a from (xa,ya) to either (xa+1,ya) or (xa,ya+1); move point b from (xb,yb) to either (xb+1,yb) or (xb,yb+1); eliminate focuses an and b. Notwithstanding, the move must be performed if there exists a line that goes through the new organizes of, another directions of b and (0,0). If not, the move can't be performed and the focuses stay at their unique directions (xa,ya) and (xb,yb), individually. Input The main line contains a solitary integer n (1≤n≤2⋅105) — the number of focuses. The I-th of the following n lines contains four integers ai,bi,ci,di (1≤ai,bi,ci,di≤109). The directions of the I-th point are xi=aibi and yi=cidi. Output :In the primary…
The supervised classification algorithm you pick will usually output a real-valued score, and you will decide upon to select out a threshold or thresholds above which to block the exercise or exhibit off increased friction. How do you select out out this threshold? Now replicate onconsideration on that you have two versions of your mannequin with one-of-a-kind parameters(e.g., extraordinarily correct regularization) or even special mannequin households (e.g., logistic regression versus random forest). Which one is better?
Analyze the worst case, the best case and average cese scenarios while executing the following algorithm using appropriate examples: Divide and conquer
Calculate the optimal value of the decision parameter p in the Bresenham's circle drawing algorithm. The stepwise procedure for implementing Bresenham's algorithm for circle drawing is delineated.
Buiding an optimization model for the building a house problem, and solve it using JuMP and Clp.Example: building a house A small sample: Let t1,t0,tm,tn,tt,ts be start times of the associated tasks. Now use the graph to write the dependency constraints: Tasks 0,m, and n can't start until task I is finished, and task I takes 3 days to finish. So the constraints are: t1+3≤3≤t0,t1+3≤tm,t1+3≤tn Task t can't start until tasks m and n are finished. Therefore: tm+1≤tt,tn+2≤tt,takes 3 days to finish. So the constraints are: Task s can't start until tasks 0 and t are finished. Therefore: t0+3≤ts,tt+3≤ts
Is it conceivable, via the use of a variety of strategies, that the overhead caused by NPT might be reduced?
Question 1: Apply A* method for finding the shortest route from S0 to We consider the problem described by the Graph below. The initial state is S0 and the goal state is S6. The heuristic of the states are presented in the table below SO 3 4 State (Si) Heuristic h(Si) si S3 SO 3 S2 3 S2 2 S3 7 S4 1 S5 4 S5 S6 S4 S6