MATH 177: Linear and Non-Linear Optimization
San Jose State University
Linear inequalities, the simplex method and other algorithms, duality, integer optimization, convex optimization, quadratic optimization, game theory. Prerequisite(s): MATH 32 or MATH 32H or MATH 32X and MATH 39 (both with a grade of C- or better). Allowed Declared Major: Mathematics major/minor or Computer Science.
Average GPA: 2.74
Grade distribution records: 465 students across 8 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 38 | 8.2% |
| A | 76 | 16.3% |
| A- | 45 | 9.7% |
| B+ | 31 | 6.7% |
| B | 78 | 16.8% |
| B- | 24 | 5.2% |
| C+ | 25 | 5.4% |
| C | 65 | 14.0% |
| C- | 21 | 4.5% |
| D+ | 4 | 0.9% |
| D | 17 | 3.7% |
| D- | 4 | 0.9% |
| F | 37 | 8.0% |
Based on 465 student grade records across 8 terms and 4 professors.
Instructors
- Mohammed Yahdi 296 students, Average GPA 2.64
- Sogol Jahanbekam 110 students, Average GPA 3.01
- Matthew Douglas Johnston 35 students, Average GPA 2.48
- Bradley W Jackson 24 students, Average GPA 2.97