Trajectory and switching-time calculation
Numerical workflows for solving continuous-time optimal-control trajectories, locating switching events, and studying how those events change with the initial state.
Portfolio · Software
Code and computational tools
Numerical implementations and visualization workflows supporting my research in dynamic optimization and control.
GitHub
Public repositories and released computational work are available through my GitHub profile.
Open GitHub ↗Computational work
Critical-region discovery and visualization
Tools for classifying parameter points, tracing region boundaries, building consistent maps, and visualizing the resulting control structure.
Continuous-time versus discrete-time comparison
Reproducible workflows for comparing region counts, trajectories, switching information, and approximation effects across formulations.
Interactive technical communication
Browser-based experiences and visual explanations that make optimization and control ideas easier to understand without removing the mathematical foundation.
Methods and tools
Python
Scientific computing
Numerical continuation
Dynamic optimization
Model predictive control
Data visualization
Reproducible workflows
Questions or collaboration ideas?
lida.lamakani@tamu.edu