Solve optimization problems with CPLEX, Gurobi, Pyomo... using linear programming, nonlinear, evolutionary algorithms...
InstructorRafael Silva PintoEditorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This educational slide illustrates various optimization problem types through visual representations including a linear programming feasible region, a network flow diagram, and complex 3D mathematical surfaces.
This course overview slide displays a laptop running a Python IDE with generated charts, listing topics like Linear Programming and Heuristics alongside tools such as Gurobi and Pandas.
This slide displays a course outline for optimization with Python, featuring a laptop screen with an IDE and a menu listing topics like Linear Programming and Heuristics.
Instruction spans multiple frameworks including Pyomo, PuLP, and Or-Tools, covering LP, MILP, and NLP applications.
The curriculum extends beyond linear programming to include metaheuristics like Genetic Algorithms and Particle Swarm Optimization.
Learners have reported difficulties with framework downloads and noted that specific lecture content regarding SCIP licensing may require updates.
One signal suggests low engagement levels and the presence of long, difficult-to-skip pauses within video lessons.
The curriculum aligns with the target audience of students and researchers, though macOS-specific guidance is noted as missing.
