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Changelog

0.2.0 (2026-04-03)

Second release with unified einsum cost model, NumPy compatibility testing, and expanded operation coverage.

New features

  • Unified einsum cost model — all einsum-like operations (einsum, dot, matmul, tensordot) now share a single cost model based on opt_einsum's contraction path optimizer
  • Symmetry-aware path finding — the opt_einsum path optimizer now factors symmetry savings into contraction ordering decisions, producing different (cheaper) paths for symmetric inputs
  • NumPy compatibility test harness — run NumPy's own test suite against mechestim via monkeypatching; 7,300+ tests passing across 7 NumPy test modules
  • Polynomial operations — polyval, polyfit, polymul, polydiv, polyadd, polysub, poly, roots, polyder, polyint with analytical FLOP costs
  • Window functions — bartlett, hamming, hanning, blackman, kaiser with per-function cost formulas
  • FFT module — fft, ifft, rfft, irfft, fft2, ifft2, fftn, ifftn, rfftn, irfftn and free helpers (fftfreq, rfftfreq, fftshift, ifftshift)
  • Client-server architecture — mechestim-client and mechestim-server packages for sandboxed competition evaluation over ZMQ
  • Global default budget — a 1e15 FLOP budget auto-activates on first use, so explicit BudgetContext is no longer required for quick scripts
  • MECHESTIM_DEFAULT_BUDGET env var — configure the global default budget amount
  • budget_live() — Rich-based live-updating budget display context manager
  • einsum_path() — inspect contraction plans with per-step symmetry savings without spending budget
  • 90%+ test coverage gate enforced in CI

Breaking changes

  • Einsum cost formula now uses product_of_all_index_dims × op_factor (op_factor=2 for inner products, 1 for outer products), matching opt_einsum convention. Previously used a different formula.
  • me.dot and me.matmul costs are now computed via the einsum cost model instead of separate formulas.

Bug fixes

  • Accept scalars and array-likes in all mechestim functions
  • Fix symmetry-aware greedy algorithm to actually use symmetry in path selection
  • Fix contract_path cost reporting for output indices
  • Correctly handle symmetric_dims propagation through multi-step contraction paths

Documentation

  • Comprehensive how-to guides for einsum, symmetry, linalg, budget planning, and debugging
  • Architecture docs for client-server model and Docker deployment
  • AI agent guide with llms.txt, ops.json, and cheat sheet
  • NumPy compatibility testing methodology docs

0.1.0 (2026-04-01)

Initial release for warm-up round.

  • Einsum with symmetry detection and FLOP counting
  • Pointwise operations (exp, log, add, multiply, etc.)
  • Reductions (sum, mean, max, etc.)
  • SVD with truncated top-k
  • Free tensor creation and manipulation ops
  • Budget enforcement via BudgetContext
  • FLOP cost query API
  • NumPy-compatible API (import mechestim as me)