| COURSE | TITLE | EFF YEAR | EFF TERM | DEPARTMENT | CREDIT HOURS | ||||
| MA381 | NONLINEAR OPTIMIZATION | 2013 | 1 | Mathematical Sciences | 3.0 (BS=0.0, ET=0.0, MA=3.0) | ||||
| SCOPE | |||||||||
| This course provides an undergraduate presentation of nonlinear topics in mathematical programming that builds on multivariable Calculus II. The emphasis of this course is on developing a conceptual understanding of the fundamental topics introduced. These topics include general convexity, convex functions, derivative-based multivariable search techniques, minima and maxima of convex functions, gradients, hessian matrices, Lagrange Multipliers, Fritz-John and Kuhn-Tucker optimality conditions, and constrained and unconstrained optimization. Computer software is used to explore and expose various key ideas throughout the course. | |||||||||
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| SPECIAL REQUIREMENTS: | |||||||||
| One (or more) special problem(s). | |||||||||
| TYPE | COURSE | EFF YEAR | EFF TERM | TRACK | RED BOOK FLG |
| PRE REQUISITE | |||||
| MA205 | 2003 | 1 | 1 | Y | |
| MA255 | 2003 | 2 | 2 | Y | |
| AYT | #SECT/SIZE | CPBLTY | ENRLD | WAIT | SEATS | CLOSED | DETAILS | ||
| 2024 - 8 | 1 | 18 | 18 | 0 | 0 | 18 | N | Hours | |
| 2025 - 1 | 1 | 18 | 18 | 8 | 0 | 10 | N | Hours | |
| 2025 - 8 | 1 | 18 | 18 | 0 | 0 | 18 | N | Hours | |
| 2026 - 1 | 2 | 18 | 36 | 21 | 0 | 15 | N | Hours | |
| 2026 - 8 | 1 | 18 | 18 | 0 | 0 | 18 | N | Hours | |
| 2027 - 1 | 2 | 18 | 36 | 29 | 0 | 7 | N | Hours | |
| COURSE | TITLE | EFF YEAR | EFF TERM | DEPARTMENT | CREDIT HOURS | ||||
| MA381 | NONLINEAR OPTIMIZATION | 2003 | 1 | Mathematical Sciences | 3.0 (BS=0.0, ET=0.0, MA=0.0) | ||||
| SCOPE | |||||||||
| This course provides an undergraduate presentation of nonlinear topics in mathematical programming that builds on multivariable Calculus II. The emphasis of this course is on developing a conceptual understanding of the fundamental topics introduced. These topics include general convexity, convex functions, derivative-based multivariable search techniques, minima and maxima of convex functions, gradients, hessian matrices, Lagrange Multipliers, Fritz-John and Kuhn-Tucker optimality conditions, and constrained and unconstrained optimization. Computer software is used to explore and expose various key ideas throughout the course. | |||||||||
|
|||||||||
| SPECIAL REQUIREMENTS: | |||||||||
| One (or more) special problem(s). | |||||||||
| TYPE | COURSE | EFF YEAR | EFF TERM | TRACK | RED BOOK FLG |
| PRE REQUISITE | |||||
| MA205 | 2003 | 1 | 1 | Y | |
| MA255 | 2003 | 2 | 2 | Y | |
| COURSE | TITLE | EFF YEAR | EFF TERM | DEPARTMENT | CREDIT HOURS | ||||
| MA381 | NONLINEAR OPTIMIZATION | 1993 | 1 | Mathematical Sciences | 3.0 (BS=0.0, ET=0.0, MA=0.0) | ||||
| SCOPE | |||||||||
| This course provides an undergraduate presentation of nonlinear topics in mathematical programming that builds on multivariable Calculus II. The emphasis of this course is on developing a conceptual understanding of the fundamental topics introduced. These topics include general convexity, convex functions, derivative-based multivariable search techniques, minima and maxima of convex functions, gradients, hessian matrices, Lagrange Multipliers, Fritz-John and Kuhn-Tucker optimality conditions, and constrained and unconstrained optimization. Computer software is used to explore and expose various key ideas throughout the course. | |||||||||
|
|||||||||
| SPECIAL REQUIREMENTS: | |||||||||
| One special problem in optimization. | |||||||||
| TYPE | COURSE | EFF YEAR | EFF TERM | TRACK | RED BOOK FLG |
| PRE REQUISITE | |||||
| MA205 | 1991 | 2 | 1 | Y | |
| MA205X | 1991 | 2 | 2 | Y | |
| MA255 | 2000 | 2 | 3 | Y | |