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Efficiency is a design constraint, not an afterthought
The course treats runtime and memory as part of the problem statement. A correct algorithm can still be the wrong answer if it scales badly.
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01:198:344 · Computer Science · School 01 — New Brunswick School of Arts and Sciences
Design and Analysis of Computer Algorithms studies how to design, analyze, and justify efficient algorithms, moving from asymptotic complexity and correctness arguments into sorting, selection, graph algorithms, hard problems, NP-completeness, and approximation.
Can I take it?
Data Structures · Introduction To Discrete Structures II · Mathematical Theory Of Probability
How hard?
Students rate teaching
SIRS · 2,547 responses · 2016–2025
Spring 2027
4 open in snapshot · 1 instructor
When it meets
Busch
Checked-in sections · current seat counts are unavailable.
AI-generated overview
Higher means more demanding · AI summary
5 units · topic sequence may vary by instructor
The course usually starts by making algorithm work rigorous. Expect asymptotic notation, worst-case versus average-case reasoning, and proof patterns that justify why an algorithm really solves the problem it claims to solve.
Early core material turns classic problems into recurrence-driven designs. Recent Rutgers course pages emphasize merge sort, quicksort, selection, Karatsuba-style ideas, and the Master Theorem as the bridge between code structure and runtime.
The middle of the course shifts from analysis alone to design patterns. Heaps, hashing, greedy strategies, and dynamic programming show how the right state representation can turn a hard-looking problem into a structured solution.
Graph problems are a major application area because they combine representation choices with reusable algorithmic templates. Rutgers topic lists highlight traversal, shortest paths, spanning structures, connectivity, and related reductions.
A defining late-course move is learning when efficient exact algorithms are unlikely. Hard problems such as satisfiability, traveling salesman, and knapsack motivate reductions, NP-completeness, and approximation as a realistic design response.
Public r/rutgers discussion consistently describes 344 as more theory-heavy than many students first expect: less about building big software systems and more about mathematical reasoning, proofs, recurrence solving, and recognizing algorithm-design patterns under exam pressure.
The course treats runtime and memory as part of the problem statement. A correct algorithm can still be the wrong answer if it scales badly.
Many topics look different on the surface, but they reuse a small set of design ideas such as recursion trees, subproblem reuse, exchange arguments, and graph exploration.
Correctness arguments are not formal decoration. They clarify what invariant, recurrence, or structural claim the algorithm relies on.
Once NP-completeness enters the picture, the goal may shift from exact polynomial-time solutions to approximation, heuristics, or careful problem restrictions.
Common pitfall
Instead
Practice explaining why the recurrence, greedy choice, or state definition is the right one for the problem.
Common pitfall
Instead
Write the invariant or induction idea while designing the algorithm so the proof grows out of the same structure.
Common pitfall
Instead
For DP and divide-and-conquer problems, define subproblems and transitions first, then implement.
Common pitfall
Instead
When reductions and NP-completeness appear, ask whether the right answer is approximation, special cases, or a proof of hardness.
4 open · Busch
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0914497 | Open | Gavva | Wednesday 12:10 PM-1:30 PM at ARC 103; Friday 2:00 PM-3:20 PM at ARC 103; Thursday 5:55 PM-6:50 PM at SEC 207ARC 103SEC 207 | Busch |
| 1014498 | Open | Gavva | Wednesday 12:10 PM-1:30 PM at ARC 103; Friday 2:00 PM-3:20 PM at ARC 103; Thursday 12:25 PM-1:20 PM at HLL 116ARC 103HLL 116 | Busch |
| 1114499 | Open | Gavva | Wednesday 12:10 PM-1:30 PM at ARC 103; Friday 2:00 PM-3:20 PM at ARC 103; Thursday 7:45 PM-8:40 PM at SEC 209ARC 103SEC 209 | Busch |
| 1214500 | Open | Gavva | Wednesday 12:10 PM-1:30 PM at ARC 103; Friday 2:00 PM-3:20 PM at ARC 103; Thursday 4:05 PM-5:00 PM at SEC 209ARC 103SEC 209 | Busch |
Historical student surveys
Teaching
3.95
Course quality
3.93
Response rate
34.2%
Coverage
48 offerings · 2016–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Yongfeng Zhang | 11 | 4.27 | 4.21 |
| Sepehr Assadi | 4 | 4.70 | 4.53 |
| Kalantari B | 4 | 3.25 | 3.33 |
| Bahman Kalantari | 4 | 2.78 | 3.28 |
| Aaron Bernstein | 3 | 4.77 | 4.57 |
| Surya Teja Gavva | 3 | 4.37 | 4.20 |
Catalog and planning context
Credits
4
Current campuses
Busch
Current availability
4 open of 4
Catalog terms
Fall, Spring, Summer
Core codes
None listed
Loaded terms
6
((01:198:112 or 14:332:351) and (01:198:206))<em> OR </em> ((01:198:112 or 14:332:351) and (01:640:477))<em> OR </em> ((01:198:112 or 14:332:351) and (14:332:321))<em> OR </em> ((01:198:112 or 14:332:351) and (14:332:226))
No direct degree-list membership appears in the checked-in requirement index.