01:198:344
COMPUTER SCIENCE
AI-generated overview
Official materials stress complexity analysis and correctness, and student discussion repeatedly describes 344 as math- and proof-heavy.
Rutgers lists regular exercises and about six small programs, and recent course pages add recurring quizzes plus substantial assignment work.
The syllabus moves from asymptotics and recurrences into greedy methods, DP, graphs, and NP-completeness within one semester.
The official workload mentions several small programs, but the course is centered more on exercises and proofs than on large build-style projects.
Rutgers lists quizzes, a midterm, and a final, and student threads repeatedly identify exams as a major source of pressure.
Complexity analysis, recurrence solving, proofs, and NP-completeness make the course strongly quantitative and theoretical.
Students usually need to derive and justify ideas, so pattern recognition and proof skill matter more than rote memorization.
The course focuses on design principles, reductions, invariants, and asymptotic reasoning rather than concrete software systems alone.
Rutgers requires both Data Structures and Discrete II, and student prep advice consistently points back to those exact foundations.
Official materials emphasize exercises, programs, quizzes, and proofs more than large required reading loads.
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.
Advice threads often call the course proof-heavy or math-heavy, especially compared with project-centered CS electives. Students who are comfortable with Discrete Structures and recurrence reasoning seem to find the transition smoother.
Public prep advice clusters around reviewing asymptotic notation, recursion, sorting/searching, graph basics, and the logic habits from Data Structures and Discrete II rather than trying to pre-learn every advanced topic.
Discussion about the class often centers on midterms, curves, and being surprised by how much explanation or proof-style reasoning is expected, which suggests that passive reading is usually not enough preparation.
Official Rutgers materials mention small programs, but student discussion still frames the harder part as choosing the right idea and justifying it clearly, not only translating a known algorithm into code.
A practical chapter-by-chapter view from foundations to applications.
Module 1
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.
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.
Practice explaining why the recurrence, greedy choice, or state definition is the right one for the problem.
Write the invariant or induction idea while designing the algorithm so the proof grows out of the same structure.
For DP and divide-and-conquer problems, define subproblems and transitions first, then implement.
When reductions and NP-completeness appear, ask whether the right answer is approximation, special cases, or a proof of hardness.
3 open · Livingston
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0111592 | Closed | Zhang, Yongfeng | Wednesday 12:10 PM-1:30 PM at LSH A102; Friday 2:00 PM-3:20 PM at LSH A102; Wednesday 4:05 PM-5:00 PM at BE 250LSH A102BE 250 | Livingston |
| 0211593 | Closed | Zhang, Yongfeng | Wednesday 12:10 PM-1:30 PM at LSH A102; Friday 2:00 PM-3:20 PM at LSH A102; Friday 4:05 PM-5:00 PM at BE 250LSH A102BE 250 | Livingston |
| 0311594 | Closed | Zhang, Yongfeng | Wednesday 12:10 PM-1:30 PM at LSH A102; Friday 2:00 PM-3:20 PM at LSH A102; Wednesday 5:55 PM-6:50 PM at BE 250LSH A102BE 250 | Livingston |
| 0411595 | Closed | Zhang, Yongfeng | Wednesday 12:10 PM-1:30 PM at LSH A102; Friday 2:00 PM-3:20 PM at LSH A102; Friday 4:05 PM-5:00 PM at LSH B269LSH A102LSH B269 | Livingston |
| 0611596 | Open | Szegedy, Mario | Monday 5:40 PM-7:00 PM at TIL 254; Wednesday 5:40 PM-7:00 PM at TIL 254; Monday 9:35 PM-10:30 PM at BE 250TIL 254BE 250 | Livingston |
| 0711597 | Open | Szegedy, Mario | Monday 5:40 PM-7:00 PM at TIL 254; Wednesday 5:40 PM-7:00 PM at TIL 254; Wednesday 7:45 PM-8:40 PM at TIL 246TIL 254TIL 246 | Livingston |
| 0811598 | Open | Szegedy, Mario | Monday 5:40 PM-7:00 PM at TIL 254; Wednesday 5:40 PM-7:00 PM at TIL 254; Monday 7:45 PM-8:40 PM at LSH B269TIL 254LSH B269 | Livingston |
| 1211599 | Closed | Biswas, Arpita | Tuesday 12:10 PM-1:30 PM at TIL 232; Friday 12:10 PM-1:30 PM at TIL 232; Friday 2:15 PM-3:10 PM at BE 250TIL 232BE 250 | Livingston |
| 1311600 | Closed | Biswas, Arpita | Tuesday 12:10 PM-1:30 PM at TIL 232; Friday 12:10 PM-1:30 PM at TIL 232; Tuesday 4:05 PM-5:00 PM at BE 252TIL 232BE 252 | Livingston |
| 1411601 | Closed | Biswas, Arpita | Tuesday 12:10 PM-1:30 PM at TIL 232; Friday 12:10 PM-1:30 PM at TIL 232; Tuesday 2:15 PM-3:10 PM at BE 252TIL 232BE 252 | Livingston |
Historical student surveys
Teaching
3.95
Course quality
3.93
Response rate
34.2%
Coverage
48 offerings · 2016–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Zhang, Yongfeng | 11 | 4.27 | 4.21 |
| Assadi, Sepehr | 4 | 4.70 | 4.53 |
| Kalantari B | 4 | 3.25 | 3.33 |
| Kalantari, Bahman | 4 | 2.78 | 3.28 |
| Bernstein, Aaron | 3 | 4.77 | 4.57 |
| Gavva | 3 | 4.37 | 4.20 |
Catalog and planning context
Credits
4
Current campuses
Livingston
Current availability
3 open of 10
Catalog terms
Fall, Spring, Summer
Core codes
None listed
Loaded terms
5
((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.
01:198:344
COMPUTER SCIENCE
AI-generated overview
Official materials stress complexity analysis and correctness, and student discussion repeatedly describes 344 as math- and proof-heavy.
Rutgers lists regular exercises and about six small programs, and recent course pages add recurring quizzes plus substantial assignment work.
The syllabus moves from asymptotics and recurrences into greedy methods, DP, graphs, and NP-completeness within one semester.
The official workload mentions several small programs, but the course is centered more on exercises and proofs than on large build-style projects.
Rutgers lists quizzes, a midterm, and a final, and student threads repeatedly identify exams as a major source of pressure.
Complexity analysis, recurrence solving, proofs, and NP-completeness make the course strongly quantitative and theoretical.
Students usually need to derive and justify ideas, so pattern recognition and proof skill matter more than rote memorization.
The course focuses on design principles, reductions, invariants, and asymptotic reasoning rather than concrete software systems alone.
Rutgers requires both Data Structures and Discrete II, and student prep advice consistently points back to those exact foundations.
Official materials emphasize exercises, programs, quizzes, and proofs more than large required reading loads.
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.
Advice threads often call the course proof-heavy or math-heavy, especially compared with project-centered CS electives. Students who are comfortable with Discrete Structures and recurrence reasoning seem to find the transition smoother.
Public prep advice clusters around reviewing asymptotic notation, recursion, sorting/searching, graph basics, and the logic habits from Data Structures and Discrete II rather than trying to pre-learn every advanced topic.
Discussion about the class often centers on midterms, curves, and being surprised by how much explanation or proof-style reasoning is expected, which suggests that passive reading is usually not enough preparation.
Official Rutgers materials mention small programs, but student discussion still frames the harder part as choosing the right idea and justifying it clearly, not only translating a known algorithm into code.
A practical chapter-by-chapter view from foundations to applications.
Module 1
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.
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.
Practice explaining why the recurrence, greedy choice, or state definition is the right one for the problem.
Write the invariant or induction idea while designing the algorithm so the proof grows out of the same structure.
For DP and divide-and-conquer problems, define subproblems and transitions first, then implement.
When reductions and NP-completeness appear, ask whether the right answer is approximation, special cases, or a proof of hardness.
3 open · Livingston
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0111592 | Closed | Zhang, Yongfeng | Wednesday 12:10 PM-1:30 PM at LSH A102; Friday 2:00 PM-3:20 PM at LSH A102; Wednesday 4:05 PM-5:00 PM at BE 250LSH A102BE 250 | Livingston |
| 0211593 | Closed | Zhang, Yongfeng | Wednesday 12:10 PM-1:30 PM at LSH A102; Friday 2:00 PM-3:20 PM at LSH A102; Friday 4:05 PM-5:00 PM at BE 250LSH A102BE 250 | Livingston |
| 0311594 | Closed | Zhang, Yongfeng | Wednesday 12:10 PM-1:30 PM at LSH A102; Friday 2:00 PM-3:20 PM at LSH A102; Wednesday 5:55 PM-6:50 PM at BE 250LSH A102BE 250 | Livingston |
| 0411595 | Closed | Zhang, Yongfeng | Wednesday 12:10 PM-1:30 PM at LSH A102; Friday 2:00 PM-3:20 PM at LSH A102; Friday 4:05 PM-5:00 PM at LSH B269LSH A102LSH B269 | Livingston |
| 0611596 | Open | Szegedy, Mario | Monday 5:40 PM-7:00 PM at TIL 254; Wednesday 5:40 PM-7:00 PM at TIL 254; Monday 9:35 PM-10:30 PM at BE 250TIL 254BE 250 | Livingston |
| 0711597 | Open | Szegedy, Mario | Monday 5:40 PM-7:00 PM at TIL 254; Wednesday 5:40 PM-7:00 PM at TIL 254; Wednesday 7:45 PM-8:40 PM at TIL 246TIL 254TIL 246 | Livingston |
| 0811598 | Open | Szegedy, Mario | Monday 5:40 PM-7:00 PM at TIL 254; Wednesday 5:40 PM-7:00 PM at TIL 254; Monday 7:45 PM-8:40 PM at LSH B269TIL 254LSH B269 | Livingston |
| 1211599 | Closed | Biswas, Arpita | Tuesday 12:10 PM-1:30 PM at TIL 232; Friday 12:10 PM-1:30 PM at TIL 232; Friday 2:15 PM-3:10 PM at BE 250TIL 232BE 250 | Livingston |
| 1311600 | Closed | Biswas, Arpita | Tuesday 12:10 PM-1:30 PM at TIL 232; Friday 12:10 PM-1:30 PM at TIL 232; Tuesday 4:05 PM-5:00 PM at BE 252TIL 232BE 252 | Livingston |
| 1411601 | Closed | Biswas, Arpita | Tuesday 12:10 PM-1:30 PM at TIL 232; Friday 12:10 PM-1:30 PM at TIL 232; Tuesday 2:15 PM-3:10 PM at BE 252TIL 232BE 252 | Livingston |
Historical student surveys
Teaching
3.95
Course quality
3.93
Response rate
34.2%
Coverage
48 offerings · 2016–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Zhang, Yongfeng | 11 | 4.27 | 4.21 |
| Assadi, Sepehr | 4 | 4.70 | 4.53 |
| Kalantari B | 4 | 3.25 | 3.33 |
| Kalantari, Bahman | 4 | 2.78 | 3.28 |
| Bernstein, Aaron | 3 | 4.77 | 4.57 |
| Gavva | 3 | 4.37 | 4.20 |
Catalog and planning context
Credits
4
Current campuses
Livingston
Current availability
3 open of 10
Catalog terms
Fall, Spring, Summer
Core codes
None listed
Loaded terms
5
((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.
