Minimizeaz

Minimizeaz is a software library and modeling concept designed to solve optimization problems that involve angular parameters, with a focus on azimuth (AZ). It provides numerical optimization routines that account for angular periodicity and wrap-around at 0/360 degrees. It supports objective functions common in navigation, robotics, and geospatial applications, such as alignment error, sensor calibration, and tracking accuracy, and offers facilities to enforce bounds and constraints on angular variables. The interface mirrors established optimization libraries, offering derivative-based and derivative-free methods, along with utilities for coordinate conversions and angular distance calculations.

Origin and development: Minimizeaz emerged from robotics and geospatial toolchains to address the challenges of optimizing over angles. It has been implemented in multiple languages and released as open-source software with community contributions and public issue trackers.

Algorithmic approach: Minimizeaz treats angular variables by mapping them to a canonical domain and using circular distance measures. It implements gradient-based methods such as L-BFGS-B with angular constraints, and non-smooth or non-convex strategies such as Nelder-Mead or simulated annealing. It supports bounds on azimuth (commonly 0 to 360 degrees) and elevation if present, and provides helpers for coordinate transformations and visualization of objective landscapes over azimuth and elevation.

Usage: Typical workflow involves defining an objective function that takes azimuth (and optionally elevation) as input, specifying bounds, selecting an optimization method, and invoking the solver. Output includes the optimal azimuth, optimal objective value, and diagnostic information like iterations and convergence status.

Reception: In its niche, minimizeaz is valued for handling angular wrap-around more robustly than generic optimizers. Some users compare its convenience and clarity against general-purpose tools, while performance and feature parity with broader libraries are common discussion points.