Radical Numerics provides high‑precision numerical computing libraries with arbitrary‑precision arithmetic, interval analysis, and validated solvers that integrate with Python, MATLAB, and C++. The tools offer drop‑in replacements for standard math functions, automatic precision tuning, and parallel execution support, helping engineers and researchers improve simulation accuracy without major code changes.
Funding
Funding not disclosed
Founders
Product
Problem
Engineers and scientists often rely on standard floating‑point libraries that provide limited precision, leading to numerical instability and inaccurate results in high‑complexity simulations and algorithm development.
Solution
Radical Numerics offers a suite of high‑precision numerical computing libraries that integrate seamlessly with popular programming environments such as Python, MATLAB, and C++. The libraries provide arbitrary‑precision arithmetic, interval analysis, and validated solvers, allowing users to reduce rounding errors and improve the reliability of their computational models. By exposing a familiar API and supporting just‑in‑time compilation, the tools enable efficient development and execution of large‑scale simulations without extensive code rewrites. The platform also includes profiling utilities that identify precision bottlenecks and suggest optimal precision settings, helping users balance accuracy with performance.
Target Audience
Primary customers are engineers and researchers in fields such as aerospace, automotive, energy, and computational physics who require high‑accuracy simulations and robust algorithm development.
Features
- Arbitrary‑precision arithmetic kernels supporting up to 10,000 decimal digits
- Interval arithmetic and validated solvers for rigorous error bounds
- Native bindings for Python (NumPy‑compatible), MATLAB, and C/C++ with just‑in‑time compilation
- Automatic precision tuning and profiling tools that highlight numerical instability
- Compatibility with existing codebases via drop‑in replacements for standard math functions
- Parallel execution support leveraging OpenMP and GPU offload where applicable