43-Suppose that you implement an algorithm to solve a problem. It works correctly and quickly for small inputs, but you notice that your program runs much slower as the input size grows (to the point of becoming unusable). In fact, each time you increase the input size by one the execution time of your program roughly doubles! Which of the following options is the most effective way of addressing this issue? You may assume that program outputs which are close to optimal are acceptable.a.Purchase a faster computer---this is a worthwhile investment as a programmer and will resolve this kind of issue.b.Design a new approximate algorithm with lower time complexity.c.Remove comments and any other non-essential elements in your source code that may be slowing things down.d.Switch to a more efficient programming language, such as machine code, because although Python is readable it is also too slow.e.Invoke Python with the --linear-time option to force your program to run in linear time, O(n).

Suppose that you implement an algorithm to solve a problem. It works correctly and quickly for small inputs, but you notice that your program runs much slower as the input size grows (to the point of becoming unusable). In fact, each time you increase the input size by one the execution time of your program roughly doubles! Which of the following options is the most effective way of addressing this issue? You may assume that program outputs which are close to optimal are acceptable

Suppose that you implement an algorithm to solve a problem. It works correctly and quickly for small inputs, but you notice that your program runs much slower as the input size grows (to the point of becoming unusable). In fact, each time you increase the input size by one the execution time of your program roughly doubles! Which of the following options is the most effective way of addressing this issue? You may assume that program outputs which are close to optimal are acceptable

C++ Programming: From Problem Analysis to Program Design

8th Edition

ISBN:9781337102087

Author:D. S. Malik

Publisher:D. S. Malik

Chapter10: Classes And Data Abstraction

Section: Chapter Questions

Problem 19PE

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Computational Systems

The computation systems can be defined as the systems that are capable of solving a problem that includes calculations either mathematical or logical, and are able to produce the result as an output. For example, a simple calculator that is given a set of numbers and operators as input and produces the result as an output can be defined as a computational system. The above example is a simple illustration only, the computational systems range from simple devices to complex devices such as the systems that control the SpaceX companies, Starlink satellites which are being quite recently seen in the skies at many places globally.

Question

43-

Purchase a faster computer---this is a worthwhile investment as a programmer and will resolve this kind of issue.

Design a new approximate algorithm with lower time complexity.

Remove comments and any other non-essential elements in your source code that may be slowing things down.

Switch to a more efficient programming language, such as machine code, because although Python is readable it is also too slow.

Invoke Python with the --linear-time option to force your program to run in linear time, O(n).

Process or set of rules that allow for the solving of specific, well-defined computational problems through a specific series of commands. This topic is fundamental in computer science, especially with regard to artificial intelligence, databases, graphics, networking, operating systems, and security.

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