lulzprime v0.2.0

This release delivers significant performance improvements, usability enhancements, and infrastructure upgrades while maintaining stdlib-only purity and exact contract compliance.

Contract Compliance

• Meissel-Lehmer π(x) backend: ENABLE_LEHMER_PI now enabled by default
  • Sublinear O(x^(2/3)) prime counting for large x
  • Exact Legendre formula implementation with memoization
  • Dedicated lehmer.py module with comprehensive tests
• Forecast refinement levels: Extended support for refinement_level parameter
  • Level 2: Higher-order PNT terms for <0.2% error at n=10^8
  • Level 3: Implemented and tested for ultra-precise forecasting
  • Maintains backward compatibility (Level 1 default)

Performance

• Log caching: LRU cache (maxsize=2048) for log_n() and log_log_n() functions
  • 25-35% reduction in simulation time for N≥10^6
  • Cache hit rate >95% in typical workloads
• Generator mode: Added as_generator parameter to simulate()
  • Memory reduction from O(N) to O(1) for streaming workloads
  • Maintains determinism: same seed yields identical sequence
  • 12 new tests validating equivalence and memory efficiency
• Dynamic β annealing: Added anneal_tau parameter to simulate()
  • Reduces early transient variance, improves convergence stability
  • 14 new tests validating annealing behavior
• CDF gap sampling: Replaced random.choices() with CDF + binary search
  • Performance improvement: O(k) → O(log k) per sample (~7-8× faster)
  • Maintains exact probability distribution semantics
  • 17 new tests validating sampling correctness

Usability

• Command-line interface: Added python -m lulzprime CLI
  • Commands: resolve, pi, simulate
  • Support for --seed, --anneal-tau, --generator flags
  • Streaming output for low-memory workflows
• JSON export: New simulation export functionality
  • simulation_to_json() and simulation_to_json_string() helpers
  • CLI --json flag for exporting results to file
  • Includes metadata (n_steps, seed, anneal_tau, timestamps)

Infrastructure

• GitHub Actions CI: Automated testing on push/PR
  • Matrix testing: Python 3.10, 3.11
  • Runs full test suite (258 passing tests)
  • Mypy type checking integrated into workflow
• mypy strict type checking: Comprehensive type annotations
  • Enabled strict mode (disallow_untyped_defs, warn_return_any)
  • Fixed 17 typing errors across 5 modules
  • Python 3.10+ type hints throughout codebase

Changed

• simulate() signature now includes: as_generator (bool), anneal_tau (float | None)
• Gap sampling implementation: bisect-based for O(log k) performance
• Total tests: 169 → 258 (89 new tests)
• ENABLE_LEHMER_PI default changed from False to True

Performance Metrics

• Simulations: 20-60% faster overall
• Memory: 75% reduction with generator mode (180 MB → 45 MB for N=10^6)
• Gap sampling: ~7-8× faster per sample for typical distributions
• Forecast accuracy: <0.2% error at n=10^8 with refinement_level=2

Notes

• All features maintain stdlib-only purity (no external dependencies)
• Backward compatible: All v0.1.2 code runs unchanged on v0.2.0
• Phase 2 (Performance), Phase 3 (Usability), Phase 4 (Infrastructure) complete

LULZprime v0.1.2 — Documentation Fix

LULZprime v0.1.2 — Documentation Fix

Fixed

• Documentation: Updated all version references in README from 0.1.0 to current version (0.1.2)

Note

• No code changes from 0.1.1
• This release addresses documentation consistency
• PyPI does not allow replacing existing releases, hence patch bump to 0.1.2

Unchanged

• ✅ No algorithm changes
• ✅ No API changes
• ✅ No behavior changes
• ✅ Configuration defaults unchanged:
  • ENABLE_LEHMER_PI = False
  • LEHMER_PI_THRESHOLD = 250_000
• ✅ Test suite: 169/169 tests passing

Links

• 📦 PyPI: https://pypi.org/project/lulzprime/0.1.2/
• 📚 Documentation: https://github.com/RobLe3/lulzprime/tree/main/docs
• 🔍 Full Changelog: https://github.com/RobLe3/lulzprime/blob/main/CHANGELOG.md

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Install:

pip install lulzprime==0.1.2

Verify:

from lulzprime import resolve
print(resolve(100))  # 541

LULZprime v0.1.1 — Patch Release

LULZprime v0.1.1 — Patch Release

Fixed

• CI Reliability: Added fail-fast: false to workflow matrix for better diagnostics
• Code Formatting: Reformatted all source files with black 25.x
• Linting Cleanup: Fixed 60 ruff violations (import sorting, unused imports, deprecated typing annotations)
• Configuration: Updated ruff config to use lint.* sections (deprecation fix)
• README: Logo now renders correctly on PyPI (GitHub raw URL)

Unchanged

• ✅ No algorithm changes
• ✅ No API changes
• ✅ No behavior changes
• ✅ Configuration defaults unchanged:
  • ENABLE_LEHMER_PI = False
  • LEHMER_PI_THRESHOLD = 250_000
• ✅ Test suite: 169/169 tests passing

Links

• 📦 PyPI: https://pypi.org/project/lulzprime/0.1.1/
• 📚 Documentation: https://github.com/RobLe3/lulzprime/tree/main/docs
• 🔍 Full Changelog: https://github.com/RobLe3/lulzprime/blob/main/CHANGELOG.md

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Install:

pip install lulzprime==0.1.1

Verify:

from lulzprime import resolve
print(resolve(100))  # 541

LULZprime v0.1.0 — Reference Implementation

LULZprime v0.1.0 — Reference Implementation

This is the initial and final reference release of LULZprime.

LULZprime is a deterministic, exact prime computation library providing:

• Exact π(x) (prime counting)
• Exact n-th prime resolution via resolve(n)
• Tier A correctness guarantees
• Bit-identical deterministic results
• Extremely low memory usage
• Evidence-backed, measured performance only

This release is feature-complete and fire-and-forget.

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Highlights

• ✅ Exact Meissel–Lehmer π(x) implementation (P2 correction)
• ✅ Practical resolve(500k) in ~73 seconds (validated)
• ✅ Memory usage ~0.66–1.16 MB (≈15× reduction vs segmented sieve)
• ✅ Deterministic, integer-only core
• ✅ 169/169 tests passing
• ✅ No runtime dependencies (stdlib only)

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Maintenance Status

This project is provided as a completed reference implementation.

• No active feature development
• No performance roadmap
• Forking is the intended continuation path

If you extend or modify this code, you are the maintainer of your fork.

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What This Is Not

• ❌ Not cryptography
• ❌ Not factorization
• ❌ Not a probabilistic model
• ❌ Not intended for unbounded indices
• ❌ Not a security primitive

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Documentation & Theory

The canonical theoretical background (OMPC) is available externally at:
https://roblemumin.com/library.html

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Support

If you leverage this library in production and it helps you save money,
please consider donating approximately 1 % of the savings to organizations supporting homeless people.

This is a request, not a license condition.

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Integrity

• Defaults unchanged (`ENABLE_LEHMER_PI = False`)
• No algorithm changes
• No speculative claims
• Fully reproducible results

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Status: Stable · Verified · Reference-grade