MATH 330 Optimization
Optimization covers a broad range of problems that share a common goal - determining the values for the decision variables in a problem that will maximize (or minimize) some objective function while satisfying various constraints. Using a mathematical modeling approach, this course introduces mathematical programming techniques and applications for linear and non-linear programming, sensitivity analysis, network modeling, integer linear programming, goal programming, and multiple criteria optimization. Software is used to solve real-world problems with an emphasis on interpretability of results.
Prerequisites
4 Undergraduate credits
Effective May 7, 2026 to present
Learning outcomes
General
- Identify linear mathematical relationships that can be used as the objective function or as constraints in a linear program.
- Formulate linear and nonlinear programming models for multiple industry and business applications.
- Interpret possible outcomes of solving linear and nonlinear programs: unique optimal solution, alternative optimal solutions, infeasibility, and unboundedness.
- Analyze results of solutions of optimization problems using sensitivity analysis.
- Interpret the results of mathematical programming for decision making.