printer friendly pageCS 161: Design and Analysis of Algorithms
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. Prerequisite: 103 or 103B; 109 or
STATS 116.
Terms: Aut, Win, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors:
Hulett, R. (PI)
;
Rubinstein, A. (PI)
;
Silas, S. (PI)
...
more instructors for CS 161 »
Instructors:
Hulett, R. (PI)
;
Rubinstein, A. (PI)
;
Silas, S. (PI)
;
Wootters, M. (PI)
;
Ali Mohammadi, Y. (TA)
;
Baby, S. (TA)
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Baby, S. (TA)
;
Blanc, G. (TA)
;
Chen, J. (TA)
;
Deaton, J. (TA)
;
Effron, A. (TA)
;
Fosli, I. (TA)
;
Glaser, N. (TA)
;
Guo, N. (TA)
;
Gupta, A. (TA)
;
Hosgur, E. (TA)
;
Hulett, R. (TA)
;
Ibeling, D. (TA)
;
Jhunjhunwala, M. (TA)
;
Li, H. (TA)
;
Mu, R. (TA)
;
Murphy, D. (TA)
;
Narayanan, D. (TA)
;
Navarro, J. (TA)
;
Redondo, E. (TA)
;
Shi, A. (TA)
;
Silas, S. (TA)
;
Starosta, A. (TA)
;
Voisin, M. (TA)
;
Zhang, B. (TA)
;
Zhu, A. (TA)
CS 261: Optimization and Algorithmic Paradigms
Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to sub-linear algorithms and decision making under uncertainty. Prerequisite: 161 or equivalent.
Terms: Win
| Units: 3
Instructors:
Goel, A. (PI)
;
Ramseyer, G. (TA)
CS 268: Geometric Algorithms
Techniques for design and analysis of efficient geometric algorithms for objects in 2-, 3-, and higher dimensions. Topics: convexity, triangulations and simplicial complexes, sweeping, partitioning, and point location. Voronoi/Delaunay diagrams and their properties. Arrangements of curves and surfaces. Intersection and visibility problems. Geometric searching and optimization. Random sampling methods. Range searching. Impact of numerical issues in geometric computation. Example applications to robotic motion planning, visibility preprocessing and rendering in graphics, and model-based recognition in computer vision. Prerequisite: discrete algorithms at the level of 161. Recommended: 164.
Last offered: Autumn 2016
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