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Book Description
This book deals with designing polynomial time approximation algorithms for NPhard optimization problems. Typically, the decision versions of these problems are in NP, and are therefore NPcomplete. From the viewpoint of exact solutions, all NPcomplete problems are equally hard, since they are interreducible via polynomial time reductions. Typically, such a reduction maps optimal solutions of the given instance to optimal solutions of the transformed instance and preserves the number of solutions. Indeed, the counting versions of all known NPcomplete problems are NPcomplete, and typically the proof follows directly from the proof of NPcompleteness.
The picture looks different when these problems are studied from the viewpoint of efficiently obtaining nearoptimal solutions: polynomial time reductions do not preserve nearoptimality of solutions, and NPcomplete problems exhibit a rich set of possibilities, all the way from allowing approximability to any required degree, to essentially not allowing approximability at all.
A problem is polynomial time solvable only if it has the algorithmically relevant combinatorial structure that can be used as "footholds" to efficiently hone in on a solution. The process of designing a polynomial time algorithm is a twopronged attack: unraveling this structure in the given problem, and finding algorithmic techniques that can exploit this structure.
Although NPhard problems do not offer footholds to find optimal solutions efficiently, they may still offer footholds to find nearoptimal solutions efficiently. So, at a high level, the process of designing approximation algorithms is not very different: it still involves unraveling relevant structure and finding algorithmic techniques to exploit it. Typically, the structure turns out to be more elaborate, and often, the algorithmic techniques result from generalizing and extending sonic of the powerful algorithmic tools developed in the study of exact algorithms. On the other hand, looking at this process a little more closely, one can see that it has its own general principles.
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