Question 1

How do I perform symbolic integration with MathCore in Rust?

Accepted Answer

Use MathCore::integrate() with a string expression and variable, like MathCore::integrate('x^2', 'x') to get x^3/3. The README shows examples for both symbolic and numerical integration, including handling bounds.

Question 2

MathCore vs SymPy for Rust projects?

Accepted Answer

MathCore is native to Rust and integrates with crates like nalgebra, offering better performance in Rust ecosystems, but SymPy in Python has a more mature feature set and larger community. Choose MathCore for Rust-specific needs or SymPy for broader CAS capabilities.

Question 3

Is MathCore suitable for machine learning optimization?

Accepted Answer

Yes, it supports automatic differentiation via gradient and Hessian computation, and gradient descent, as shown in the optimization examples. However, for large-scale ML, specialized libraries like PyTorch might offer better GPU support and pre-built models.

Question 4

How accurate is the arbitrary precision arithmetic in MathCore?

Accepted Answer

It uses BigInt and BigRational for exact rational arithmetic, ensuring no precision loss in calculations like 1/3 + 1/6 = 1/2. The README demonstrates this with precision examples, but performance may decrease with very high precision.

Question 5

Can MathCore handle complex numbers in expressions?

Accepted Answer

Yes, it has built-in support for complex numbers, allowing operations like (3+4i) * (2-i) as shown in the README. It integrates complex arithmetic into symbolic expressions and solving.

Question 6

What are the performance benchmarks for MathCore?

Accepted Answer

The README cites rough numbers: expression parsing at ~1μs and differentiation at ~10μs for polynomials. It uses LTO in release builds and nalgebra with BLAS, but benchmarks are limited and may vary with expression complexity.

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