Course Description This course will cover the basic approaches and mindsets for analyzing and designing algorithms and data structures. Topics include the following: Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization.
Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. There will be no late days. However, we will drop your lowest homework grade. Note: In order to compute scores on the homework in deciding which to drop and for averaging , we will normalize the scores by the number of points possible. Topics and readings for future lectures are tentative and may be changed as the course proceeds.
The readings refer to the 3rd edition of CLRS see Resources below , but older editions should be fine as well. Algorithm design considering complexity of algorithm then it would be techniques give guidance and direction on how to create a inefficient.
Efficiency covers lots of points, including. Though there are literally thousands of [1,5] algorithms, there are very few design techniques. Instead, it is often the case that different algorithms design strategies perform well on different Be careful to differentiate between, Performances: how problem instances.
This depends on the machine, compiler, problems, because runtimes for these algorithms design etc. Complexity, how do the resource strategy are often highly variable from instance to requirements of a program or algorithm scale, i.
Choosing best algorithm design strategy is one happens, as the size of the problem being solved gets of the most difficult decisions. Algorithm design is a larger? Complexity affects performance but not the other specific method to create a mathematical process in way around.
The time required by a method is solving problems. Applied algorithm design is algorithm proportional to the number of "basic operations" that it engineering. Techniques for designing and implementing performs. Some current uses of of the algorithm design strategy.
Strategy Use employ evolving design strategy to make algorithm. Not widely Algorithm 2. Please Note : This list is not the final book list.
Kruse Book. Cormen Charles E. Leiserson and Ronald L. Rivest Book. Other Useful Links. Sc, B. Tech CSE, M. Tech branch to enhance more knowledge about the subject and to score better marks in the exam.
Algorithm Design Techniques: Iterative technique: Applications to Sorting and Searching review , their correctness, and analysis. Divide and Conquer: Application to Sorting and Searching review of binary search , merge sort, quick sort, their correctness, and analysis. Dynamic Programming: Application to various problems for reference; Weighted Interval Scheduling, Sequence Alignment, Knapsack , their correctness, and analysis.
Greedy Algorithms: Application to various problems, their correctness, and analysis.
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