A Practical Introduction to Data Structures and Algorithm Analysis: A Practical Introduction to Data Structures and Algorithm Analysis Third Edition.pdf

We study data structures so that we can learn to write more efficient programs. But
why must programs be efficient when new computers are faster every year? The
reason is that our ambitions grow with our capabilities. Instead of rendering efficiency
needs obsolete, the modern revolution in computing power and storage capability
merely raises the efficiency stakes as we computerize more complex tasks.
The quest for program efficiency need not and should not conflict with sound
design and clear coding. Creating efficient programs has little to do with “programming
tricks” but rather is based on good organization of information and good algorithms.
A programmer who has not mastered the basic principles of clear design
is not likely to write efficient programs. Conversely, “software engineering” cannot
be used as an excuse to justify inefficient performance. Generality in design can
and should be achieved without sacrificing performance, but this can only be done
if the designer understands how to measure performance and does so as an integral
part of the design and implementation process. Most computer science curricula
recognize that good programming skills begin with a strong emphasis on fundamental
software engineering principles. Then, once a programmer has learned the
principles of clear program design and implementation, the next step is to study the
effects of data organization and algorithms on program efficiency.
Approach: This book describes many techniques for representing data. These
techniques are presented within the context of the following principles:
1. Each data structure and each algorithm has costs and benefits. Practitioners
need a thorough understanding of how to assess costs and benefits to be able
to adapt to new design challenges. This requires an understanding of the
principles of algorithm analysis, and also an appreciation for the significant
effects of the physical medium employed (e.g., data stored on disk versus
main memory).

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