Having presented the general framew

Having presented the general framework for describing and analyzing algorithms,
we now present a case study in algorithm analysis to make this discussion more
concrete. Specifically, we show how to use the big-Oh notation to analyze three
algorithms that solve the same problem but have different running times.
The problem we focus on is one that is reportedly often used as a job interview
question by major software and Internet companies—the maximum subarray
problem. Here, we are given an array of integers and asked to find the subarray
whose elements have the largest sum. See the example of Figure 1.13. That is,
given array A = [a1, a2, ..., an], find indices j and k that maximize the sum

ผลลัพธ์ (อังกฤษ) 2:

Having Presented for describing and analyzing the general Framework algorithms,
we now present a Case Study in Analysis algorithm to Make this discussion more
concrete. Specifically, we Show How to use the Big-Oh notation to Analyze Three
algorithms that Solve the Same Problem but have different running times.
The Problem we Focus on is one that is reportedly often used as a job interview
question by Major Software and internet companies. -the maximum Subarray
Problem. Here, we are an array of integers Given and asked to Find the Subarray
whose Elements have the largest Sum. See the example of Figure 1.13. That is,
Given array A = [A1, A2, ..., an], J and K indices that Maximize Find the Sum.

ผลลัพธ์ (อังกฤษ) 3:

Having presented the general framework for describing and, analyzing algorithmsWe now present a case study in algorithm analysis to make this discussion more.Concrete. Specifically we show, how to use the big-Oh notation to analyze three.Algorithms that solve the same problem but have different running times.The problem we focus on is one that is reportedly often used as a job interview.Question by major software and Internet companies - the maximum subarray.Problem. Here we are, given an array of integers and asked to find the subarray.Whose elements have the largest sum. See the example of Figure 1.13, That is.Given array A = [A1, the A2,..., an], find indices J and K that maximize the sum.