Answer:
research and install new systems and networks. implement technology, directing the work of systems and business analysts, developers, support specialists and other computer-related workers. evaluate user needs and system functionality, ensuring that IT facilities meet these needs.
Explanation:
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.
Microsoft Word, Libre, and Google Docs have always enabled me to insert illustrations. Look for an image icon in the task icon, here you can select an image from your files.
Answer:
a. Locate
Explanation:
Question options (obtained on the net):
a. Locate
b. Sort
c. Filter
d. Replace
Explanation:
The largest value of the weights of the dog breed in row 43 is the maximum weight of the dog breed in the row
The 'MAX' formula or the 'Autosum' tool can be used to give the largest value in a row, or column
To locate the maximum value in MS Excel, the ADDRESS and MATCH and MAX functions are combined and entered into the blank cell on the right, specifying the same range for the MAX and MATCH functions, and a '0' for the MATCH function as well as a '1' for the ADDRESS function as follows;
=ADDRESS(MATCH(MAX(A1:E1), A1:E1, 0), 1)
Therefore, to navigate to the maximum weight, Emily should use a <em>locate</em>(ing) tool navigate to the information on the spread sheet
Spreadsheet
Description
A spreadsheet is a computer application for organization, analysis and storage of data in tabular form where Jacob can store the user’s information.
