Answer:
Greedy is an algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. Greedy algorithms are used for optimization problems. An optimization problem can be solved using Greedy if the problem has the following property: At every step, we can make a choice that looks best at the moment, and we get the optimal solution of the complete problem.
If a Greedy Algorithm can solve a problem, then it generally becomes the best method to solve that problem as the Greedy algorithms are in general more efficient than other techniques like Dynamic Programming. But Greedy algorithms cannot always be applied. For example, the Fractional Knapsack problem (See this) can be solved using Greedy, but 0-1 Knapsack cannot be solved using Greedy.
The following are some standard algorithms that are Greedy algorithms.
1) Kruskal’s Minimum Spanning Tree (MST): In Kruskal’s algorithm, we create an MST by picking edges one by one. The Greedy Choice is to pick the smallest weight edge that doesn’t cause a cycle in the MST constructed so far.
2) Prim’s Minimum Spanning Tree: In Prim’s algorithm also, we create an MST by picking edges one by one. We maintain two sets: a set of the vertices already included in MST and the set of the vertices not yet included. The Greedy Choice is to pick the smallest weight edge that connects the two sets.
3) Dijkstra’s Shortest Path: Dijkstra’s algorithm is very similar to Prim’s algorithm. The shortest-path tree is built up, edge by edge. We maintain two sets: a set of the vertices already included in the tree and the set of the vertices not yet included. The Greedy Choice is to pick the edge that connects the two sets and is on the smallest weight path from source to the set that contains not yet included vertices.
4) Huffman Coding: Huffman Coding is a loss-less compression technique. It assigns variable-length bit codes to different characters. The Greedy Choice is to assign the least bit length code to the most frequent character. The greedy algorithms are sometimes also used to get an approximation for Hard optimization problems. For example, the Traveling Salesman Problem is an NP-Hard problem. A Greedy choice for this problem is to pick the nearest unvisited city from the current city at every step. These solutions don’t always produce the best optimal solution but can be used to get an approximately optimal solution.
Answer:
What you should do is to move the motherboard jumper.
Explanation:
Based on the information given in a situation where you boot the computer system in which you find out that there was a password set on the BIOS which full meaning is BASIC INPUT/OUT SYSTEM which means that if You need to clear the set password in order for you to edit the CMOS settings which full meaning is COMPLEMENTARY METAL OXIDE SEMICONDUCTOR What you should do is for you to move the MOTHERBOARD JUMPERS reason been that jumpers will help to set or configure the MOTHERBOARD which is the most important part of a computer and the brain of a computer in order to clear the password so that CMOS setting can be edited.
Answer:
In C++:
#include<iostream>
#include<vector>
using namespace std;
int main(){
int len, num;
vector<int> vect;
cout<<"Length: ";
cin>>len;
for(int i = 0; i<len;i++){
cin>>num;
vect.push_back(num);}
vector<int>::iterator iter;
for (iter = vect.end() - 1; iter >= vect.begin(); iter--){
cout << *iter << ", ";}
}
Explanation:
This declares the length of vector and input number as integer
int len, num;
This declares an integer vector
vector<int> vect;
This prompts the user for length
cout<<"Length: ";
This gets the input for length
cin>>len;
The following iteration gets input into the vector
<em> for(int i = 0; i<len;i++){</em>
<em> cin>>num;</em>
<em> vect.push_back(num);}</em>
This declares an iterator for the vector
vector<int>::iterator iter;
The following iterates from the end to the beginning and prints the vector in reverse
<em> for (iter = vect.end() - 1; iter >= vect.begin(); iter--){</em>
<em> cout << *iter << ", ";}</em>
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<h2>Sure, I wanna talk. (: .........................</h2>
