methods of organizing large amounts of data, and algorithm analysis, the estimation of the
running time of algorithms. As computers become faster and faster, the need for programs
that can handle large amounts of input becomes more acute. Paradoxically, this requires
more careful attention to efficiency, since inefficiencies in programs become most obvious
when input sizes are large. By analyzing an algorithm before it is actually coded, students
can decide if a particular solution will be feasible. For example, in this text students look at
specific problems and see how careful implementations can reduce the time constraint for
large amounts of data from centuries to less than a second. Therefore, no algorithm or data
structure is presented without an explanation of its running time. In some cases, minute
details that affect the running time of the implementation are explored.
Once a solution method is determined, a program must still be written. As computers
have become more powerful, the problems they must solve have become larger and more
complex, requiring development of more intricate programs. The goal of this text is to teach
students good programming and algorithm analysis skills simultaneously so that they can
develop such programs with the maximum amount of efficiency.
This book is suitable for either an advanced data structures course or a first-year
graduate course in algorithm analysis. Students should have some knowledge of intermediate
programming, including such topics as pointers, recursion, and object-based
programming, as well as some background in discrete math.
