

Algorithms, 1st Edition
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* The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.
Instructor Details
This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal.
Features include: The use of boxes to strengthen the narrative: pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated.
Carefully chosen advanced topics that can be skipped in a standard one-semester course, but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence.
An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. In addition to the text, DasGupta also offers a Solutions Manual, which is available on the Online Learning Center.
"Algorithms is an outstanding undergraduate text, equally informed by the historical roots and contemporary applications of its subject. Like a captivating novel, it is a joy to read." Tim Roughgarden Stanford University
0 Prologue
1 Algorithms with Numbers
2 Divide-and-Conquer Algorithms
3 Decompositions of Graphs
4 Paths in Graphs
5 Greedy algorithms
6 Dynamic Programming
7 Linear Programming and Reductions
8 NP-complete Problems
9 Coping with NP-completeness
10 Quantum Algorithms
2 Divide-and-Conquer Algorithms
3 Decompositions of Graphs
4 Paths in Graphs
5 Greedy algorithms
6 Dynamic Programming
7 Linear Programming and Reductions
8 NP-complete Problems
9 Coping with NP-completeness
10 Quantum Algorithms
4 Paths in Graphs
5 Greedy algorithms
6 Dynamic Programming
7 Linear Programming and Reductions
8 NP-complete Problems
9 Coping with NP-completeness
10 Quantum Algorithms
6 Dynamic Programming
7 Linear Programming and Reductions
8 NP-complete Problems
9 Coping with NP-completeness
10 Quantum Algorithms
8 NP-complete Problems
9 Coping with NP-completeness
10 Quantum Algorithms
10 Quantum Algorithms
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