
Contributed to AlgebraicJulia/AlgebraicOptimization.jl by developing and refining model predictive control (MPC) features, agent-based simulations, and advanced plotting utilities over a three-month period. Leveraged Julia, JuMP, and Ipopt to implement horizon-based MPC modules with explicit state-space dynamics, target-state objectives, and convergence visualization. Enhanced code modularity and maintainability by introducing reusable plotting modules and improving documentation, while supporting data export in CSV and visualization in PNG formats. Delivered example-driven demonstrations such as flocking and moving formations, focusing on clear trajectory displays and onboarding efficiency. Maintained API stability through dependency updates and asset cleanup, emphasizing reproducibility and user accessibility.
April 2025 monthly summary for AlgebraicOptimization.jl: Delivered two feature-rich examples with enhanced plotting capabilities and documentation, updated dependencies, and improved asset hygiene. The changes strengthen demonstrability for users and contribute to onboarding efficiency, while preserving API stability.
April 2025 monthly summary for AlgebraicOptimization.jl: Delivered two feature-rich examples with enhanced plotting capabilities and documentation, updated dependencies, and improved asset hygiene. The changes strengthen demonstrability for users and contribute to onboarding efficiency, while preserving API stability.
March 2025 monthly delivery focused on strengthening MPC reliability, plotting clarity, and maintainability in AlgebraicOptimization.jl. Delivered explicit state-space refactor, improved visualization, and reusable plotting utilities, enabling clearer demonstrations and faster analysis. Prepared data-driven demonstrations with sample trajectories to support stakeholder reviews and publications.
March 2025 monthly delivery focused on strengthening MPC reliability, plotting clarity, and maintainability in AlgebraicOptimization.jl. Delivered explicit state-space refactor, improved visualization, and reusable plotting utilities, enabling clearer demonstrations and faster analysis. Prepared data-driven demonstrations with sample trajectories to support stakeholder reviews and publications.
February 2025: Consolidated model predictive control (MPC) capabilities in AlgebraicOptimization.jl. Delivered a basic MPC module 'simple_mpc' using JuMP and Ipopt with horizon-based optimization, added a target-state objective, and plotting of control inputs to visualize MPC behavior. Upgraded dependencies and corrected variable declarations to fix integration issues. Achieved initial convergence improvements by factoring state updates at each step and adding graphs; included an initial sample function (not tested) to validate the MPC workflow.
February 2025: Consolidated model predictive control (MPC) capabilities in AlgebraicOptimization.jl. Delivered a basic MPC module 'simple_mpc' using JuMP and Ipopt with horizon-based optimization, added a target-state objective, and plotting of control inputs to visualize MPC behavior. Upgraded dependencies and corrected variable declarations to fix integration issues. Achieved initial convergence improvements by factoring state updates at each step and adding graphs; included an initial sample function (not tested) to validate the MPC workflow.

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