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Computational Intractability (A Guide to Algorithmic Lower Bounds)
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$90.00
| Expected release date is Sep 22nd 2026 |
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Product Details
Author:
Erik D. Demaine, William Gasarch, Mohammad T. Hajiaghayi
Format:
Hardcover
Pages:
550
Publisher:
MIT Press (September 22, 2026)
Imprint:
The MIT Press
Release Date:
September 22, 2026
Language:
English
Audience:
General/trade
ISBN-13:
9780262550772
ISBN-10:
0262550776
Weight:
20oz
Dimensions:
7" x 9"
File:
RandomHouse-PRH_Book_Company_PRH_PRT_Onix_delta_active_D20260513T235852_156254930-20260514.xml
Folder:
RandomHouse
List Price:
$90.00
Country of Origin:
United States
Pub Discount:
65
Case Pack:
12
As low as:
$69.30
Publisher Identifier:
P-RH
Discount Code:
A
QuickShip:
Yes
Overview
A practical guide to understanding the theory and practice of computational lower bounds.
A fundamental question in computer science is: “Given a problem, how hard is it to solve?” Usually, the answer to this question lies in determining how long it will take to solve a problem as a function of the length of the input. Yet this question has two different parts, with two different answers: (1) upper bounds, which show that a problem can be solved in time T(n), and (2) lower bounds, which show that a problem cannot be solved in time T(n). In Computational Intractability, Erik Demaine, William Gasarch, and Mohammad Hajiaghayi focus on the latter, providing a guidebook to navigating lower bounds via the study of P, NP, NP-completeness, and other related notions.
Computational Intractability covers virtually all aspects of lower bounds, from parallelism to undecidability, and explores this material from the point of view of actual problems rather than classes of problems. The authors show how to prove lower bounds on problems in a wide variety of settings: polynomial time, classes likely above polynomial time (e.g., polynomial space), and classes within polynomial time (e.g., quadratic time).
A fundamental question in computer science is: “Given a problem, how hard is it to solve?” Usually, the answer to this question lies in determining how long it will take to solve a problem as a function of the length of the input. Yet this question has two different parts, with two different answers: (1) upper bounds, which show that a problem can be solved in time T(n), and (2) lower bounds, which show that a problem cannot be solved in time T(n). In Computational Intractability, Erik Demaine, William Gasarch, and Mohammad Hajiaghayi focus on the latter, providing a guidebook to navigating lower bounds via the study of P, NP, NP-completeness, and other related notions.
Computational Intractability covers virtually all aspects of lower bounds, from parallelism to undecidability, and explores this material from the point of view of actual problems rather than classes of problems. The authors show how to prove lower bounds on problems in a wide variety of settings: polynomial time, classes likely above polynomial time (e.g., polynomial space), and classes within polynomial time (e.g., quadratic time).
