Symbolica Computer Algebra System
A new Rust-based computer algebra system, Symbolica, is drawing interest for its ability to handle extremely large symbolic expressions—on the order of hundreds of megabytes to nearly a terabyte—used in areas like high‑precision particle physics calculations. Commenters compare its speed and capabilities to established tools such as SymPy, Maxima, Mathematica, Sage, and FORM, noting strengths in polynomial arithmetic, pattern matching, and integration with existing codebases, but also pointing out missing features like ODE solving. At the same time, its source-available but proprietary licensing, online activation requirements, and opaque pricing provoke skepticism, especially from those wary of depending on non‑FOSS tools for core scientific workflows.
Use cases and capabilities
- Strong focus on huge symbolic expressions, especially rational polynomials with hundreds of MB to ~1 TB size and >100M terms.
- Key real-world domain: high‑precision quantum field theory / collider physics (Feynman diagrams, QCD beta function), where intermediate expressions blow up then collapse to compact results.
- Supports streaming large expressions from disk and combining terms via external mergesort, echoing “tape drive” algorithms for big data.
- Also used for complex but comparatively small systems (e.g., 6 linear equations leading to ~80KB expressions), where simplification speed is the differentiator.
- Provides polynomial tools (Groebner bases, GCD, elimination via lex order/resultants), pattern matching, series expansion, numerical integration, and a general expression system; ODE solving is missing.
Performance and comparisons
- Multiple reports that it is significantly faster and more compact than SymPy and Maxima on some tasks; a referenced paper claims ~10× speed and ~60× less memory than Maxima for certain polynomial problems.
- Some users note similarities in motivation to earlier CAS efforts that replaced slower systems in physics research.
- Compared to Mathematica/Sage, Symbolica aims to be a fast embeddable library rather than a full ecosystem; some claim it outperforms Mathematica on pattern matching and rational polynomial manipulation.
API, language, and UX
- Python API seen as straightforward but requires explicit variable/function creation; some compare this unfavorably to Mathematica’s very terse syntax.
- C/C++ interface is currently thin and C‑like; some want idiomatic modern C++ wrappers.
- Documentation examples had minor issues (typos, missing variable declarations) but were quickly fixed.
Licensing, pricing, and source availability
- Source‑available but proprietary; students and hobbyists can use it free, professional/academic use requires a license.
- Current emphasis is on institution‑wide academic licenses; indicative price mentioned around several thousand EUR per year per site.
- Online license checks and 24‑hour offline keys drew strong criticism, especially from people needing long offline runs; author indicates willingness to relax this.
- Some argue depending on a proprietary CAS is risky; others say source availability plus small‑author responsiveness mitigates this.
- Confusion and debate around “open source” vs “source‑available” is explicitly noted.
Naming and ecosystem concerns
- Name clashes with an unrelated AI startup and general frustration about namespace conflicts in the AI/ML world.