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Change Log

v0.5.6

(under development)

/

v0.5.5

(2026-02-23)

  • improved:
  • API
    • improve usage ergonomics of DiversityMetric by making it a plain StrEnum.
    • reorganize internal, non-public-API functionality into max_div._core
    • create top-level max_div.solver, ... modules for user-facing API
  • docs
    • extend solver preset benchmarking data (U1-U4, C1-C4)
    • minor improvements to docs figures layout

v0.5.4

(2026-02-16)

  • new:

  • add --max-run-duration-minutes option to benchmark solver presets CLI command.

  • improved:

  • simplify: remove glightbox mkdocs plugin; due to limited zoom functionality and awkward ergonomics on mobile.
  • improved q10-q90 solver preset results uncertainty estimation
  • extend solver preset benchmarking data (U3, U4)

v0.5.3

(2026-02-14)

  • new:

  • add tabular solver preset benchmark results to docs

  • improved:

  • tweaks to docs figures layout

v0.5.2

(2026-02-11)

  • new:

  • docs: add solver preset benchmarking results

  • improved:

  • tweaks to docs figures layout
  • improve how solver preset benchmarks are set up and executed

v0.5.1

(2026-02-09)

  • new:

  • docs: add illustrations to all 8 solver benchmark problems & restructure for clarity

  • improved:

  • use InitRandomOneShot instead of InitFast for all presets, to allow seed to more strongly affect outcome, increasing the chance to get good results across multiple seeds
  • solver preset benchmarking functionality
    • improved scope calculations (higher accuracy for target duration; avoid too short durations)
    • improved result reporting (case-specific number formatting; no constraint score for unconstrained problems)

v0.5.0

(2026-02-01)

  • new:

  • add solver preset benchmarking capabilities to CLI

  • improved:

  • rename cli command benchmark solver run to benchmark solver strategies to accommodate future additions.

  • deprecated:

  • remove platform benchmarking functionality (formerly used to estimate duration of initialization strategies in presets)

  • internal:

  • refactor Markdown formatting functionality for easier reuse

v0.4.7

(2026-01-27)

  • improved:
  • general solver (SolverState) low-level speed optimizations (factor ~2-3x)
    • keep track of selection as a boolean numpy array, instead of a SortedSet
    • keep track of constraint membership as numpy arrays instead of Python lists
    • implement SolverState.add_many, SolverState.remove_many methods for more efficient batch modifications
  • solver presets
    • streamline RANDOM & GUIDED presets to always use 'fast' initialization, like all other presets.

v0.4.6

(2026-01-26)

  • new:
  • make adaptive Poisson-based sampling (used for swap size and # candidates meta-parameter sampling) cost-aware, by penalizing adapting towards larger, more expensive values.

  • extend solver benchmark problems A1-A5 to extended set of 4 unconstrained (U1-U4) and 4 constrained (C1-C4) problems

  • improved:

  • solver presets
    • tweaks to SMART & THOROUGH presets for faster convergence
    • increase adaptive sampling learning rate by ~10x
    • make adaptive sampling cost-aware (SMART: 0.5, THOROUGH: 0.1)
  • make solver debug info reporting (verbosity=25) more informative (a.o., added swap success %)
  • make all numbs.njit-decorated functions cached

v0.4.5

(2026-01-20)

  • new:
  • add ability to ignore diversity if constraints are not yet satisfied, the first 'f' fraction of optimization step progress
  • add new optimization strategy 'OptimSmartSwaps', leading to more robust & faster convergence across the board
  • add new solver presets:
    • RANDOM: baseline strategy for reference (using OptimRandomSwaps)
    • GUIDED former default
    • SMART: new default
    • THOROUGH: same as SMART but with significantly wider sampling ranges for swap_size, nc_add & nc_remove.

v0.4.4

(2026-01-18)

  • new:

    • support sampled parameters (using AdaptiveSampler class) in optimization strategies, similar to ParameterSchedule, but stochastic in nature.

  • improved:

    • full refactor of sampling functionality, for improved flexibility, performance & future-readiness
    • MaxDivSolver.solver(): improve granularity of verbosity parameter wrt reporting frequency, including debug mode.
    • solve CLI command: add ability to specify which preset to use.

  • deprecated:

    • remove unneeded randint_constrained_robust function and associated functionality.

v0.4.3

(2026-01-02)

  • new:

    • implement verbosity parameter in MaxDivSolver.solver() and add option for more detailed tabular progress reporting.

  • improved:

    • ci: show slow tests in CI when running tests or generating coverage (badge generation)
    • cli:
      • add benchmark platform command to benchmark platform speed
      • add solve command for easy test-running the solver on test problems
    • initialization strategies: update internal calling API in preparation of improved progress reporting

v0.4.2

(2026-01-02)

  • improved:
    • TargetDuration now implements __eq__ and more informative __repr__ and __str__ for time-based durations.
    • MaxDivSolverBuilder: add config options to default preset + improve test coverage
    • estimate_platform_speed
      • move to internal.benchmarking
      • improve implementation for maintainability & encapsulation
    • fix slowest & a few flaky tests

v0.4.1

(2025-12-31)

  • new:

    • notebooks for automated estimation of time models of init. and optim. strategies for different problem sizes

  • improved:

    • make default preset in MaxDivSolverBuilder smart, taking into account expected duration of strategies
    • make solver benchmarks explicitly tied to presets, reducing duplication of code

  • fixed:

    • corner case where randint_constrained could return duplicate samples due to int32 overflow/underflow wrap-around

v0.4.0

(2025-12-28)

  • new:
    • implement initial default preset in MaxDivSolverBuilder, allowing low-threshold initialization of a solver for any given problem.

v0.3.9

(2025-12-28)

  • new:

    • solver strategies
    • implement OptimGuidedSwaps optimization strategy, the intended workhorse strategy for the solver
    • extend solver benchmarking implementation & docs with new strategy

  • improved:

    • solver strategies
      • switch to more generic and faster exponential_selectivity in initialization strategies
    • solver benchmarking
      • streamline solver benchmarking implementation
      • add options --optimization-only and --initialization-only for targeted tests
      • sort vectors in solver benchmark problems such as to penalize degenerate/naive problem initialization (i.e. make the first k vectors not randomly scattered, but clustered together)

v0.3.8

(2025-12-25)

  • new:

    • solver framework
    • implement SwapBasedOptimizationStrategy base class for swap-based optimization strategies
    • solver strategies
    • implement InitDummy baseline initialization strategy
    • implement OptimRandomSwaps baseline optimization strategy
    • benchmarks
    • extend solver benchmarks with testing of optimization strategies + test up to larger problem sizes

  • improved:

    • solver framework
    • speed improvement of score computation in case of soft constraints
    • improve unit test coverage & depth of SolverState
    • low-level math
    • speed improvement of fast exponential and power function approximations by up to ~40%, leading to speed improvements in modify_p_selectivity methods of up to ~25-30%

v0.3.7

(2025-12-24)

  • new:

    • solver framework
    • extend OptimizationStrategy implementation to support scheduled parameters, that evolve according to a schedule during multi-iteration execution of a strategy
    • low-level math
    • add exponential p-selectivity modification method
    • add truncated Poisson sampling

  • improved:

    • docs
    • tweak layout of solver benchmark docs
    • diversity metrics
    • speed improvement of geomean_separation_approx (~60%)
    • initialization strategies
    • sampling heuristics of InitEager, leading to slightly higher (~5%) diversity scores and slightly faster execution (~10%)
    • reduce unnecessary array copying in InitRandomBatched and InitRandomEager

v0.3.6

(2025-12-22)

  • new:
    • implement InitEager initialization strategy
    • core implementation
    • extend solver benchmarking implementation & docs with new strategy

v0.3.5

(2025-12-22)

  • new:

    • implement InitRandomBatched initialization strategy
    • core implementation
    • extend solver benchmarking implementation & docs with new strategy
    • extended functionality for randint_constrained
    • add k_context parameter to enable sampling in batches
    • add i_forbidden parameter to have more flexibility on how to sample from non-selected subsets of ranges
    • refactor modify_p_selectivity methods
    • unify various implementations under single API
    • allow in-place modification, for optimal speed
    • add fast-power based implementation (method=20)
    • add accuracy estimation to modify_p_selectivity benchmark

  • improved:

    • rename initialization strategy InitOneShotRandom -> InitRandomOneShot, in preparation of other random init strategies
    • benchmark internal CLI command
      • Redirect output to file using --file flag
      • add all benchmark that runs all in one
      • add make command to automatically regenerate benchmark results in docs
    • improve accuracy of modify_p_selectivity method 20 (recalibrated coefficients) & unify calibration methods
    • speed up modify_p_selectivity method 100 by ~10%

v0.3.4

(2025-12-13)

  • new:

    • Add 5th solver benchmark problem with simpler constraints, to complete the spectrum
    • Add benchmarking results to documentation & add make command to automatically regenerate benchmark results

  • improved:

    • benchmark solver run CLI command
      • Add constraint score reporting
      • Add uncertainty ranges to reported metrics
      • Add --turbo flag and --speed parameter
      • Redirect output to file using --file flag
      • Allow benchmarking all test problems with single command
      • Improve test coverage

v0.3.3

(2025-12-12)

  • new:

    • implement InitOneShotRandom initialization strategy
    • make SolverStep, InitializationStrategy & OptimizationStrategy instances have a settable random seed
    • add distinction between internal and solver benchmarks in CLI & implement benchmark solver commands

  • improved:

    • add initialization of SolverState to the reported results
    • reliability & testing of DiversityMetric implementations for sub-standard array sizes
    • improve docs structure

v0.3.2

(2025-12-11)

  • new:

    • Framework for built-in benchmark suite for MaxDivSolver problems & strategies
    • First 4 benchmark problems (A1, A2, A3, A4)

  • improved:

    • structure of CLI implementation; improving encapsulation of internal, low-level, CLI-only benchmarking functionality
    • various low-level algorithmic tweaks & test coverage improvements

v0.3.1

(2025-12-10)

  • new:

    • add support for Python 3.14
    • MaxDivProblem class, so problems can be formulated outside of the MaxDivSolver and MaxDivSolverBuilder classes, paving the way for a built-in benchmark suite.

  • improved:

    • improve consistency of dimension notations n, d, k, m across codebase

v0.3.0

(2025-12-10)

  • new:

    • methods for modifying 'selectivity' of an array of probabilities (more or less uniform)
      • modify_p_selectivity_power & modify_p_selectivity_pwl2
      • add benchmarking of these methods to CLI & docs
    • make strategies aware of progress fraction within execution of a SolverStep to allow scheduling various parameters (constraint softness, wide vs selective search, ...)

  • improved:

    • add caching of Score object in SolverState to avoid redundant score computations when state hasn't changed

v0.2.6

(2025-12-09)

  • new:

    • full support for structured multi-component scoring & diversity tie-breaker metrics
      • implement structured Score object with optional div_tie_breakers field
      • add support for manual or preset tie-breaker metrics in MaxDivSolverBuilder
      • integrate update Score class with SolverState & Solver implementations
    • add initial support for soft constraint handling
    • make a SolverStep return score checkpoints correctly via SolverStepResult
    • make MaxDivSolver return extensive meta-data via MaxDivSolution
    • add non_zero_separation_frac to DiversityMetric implementations and benchmarking suite

  • improved:

    • ~20% speed improvement of min_separation diversity metric

v0.2.5

(2025-12-06)

  • improved:
    • Improve progress tracking implementation for future use in MaxDivSolver overall progress reporting
    • Introduction of SolverStep, combining a strategy & a duration
    • Introduction of Score class for more structured scoring enabling more advanced future strategies

v0.2.4

(2025-12-05)

  • new:

    • SolverStrategy progress bar support
    • First version of main MaxDivSolver loop

  • improved:

    • test coverage of SolverState and SolverStrategy modules

v0.2.3

(2025-12-04)

  • new:
    • add SolverState snapshot management, so SolverStrategy modules can perform trial-modifications to the state.
    • implement first version of SolverStrategy API

v0.2.2

(2025-12-03)

  • new:

    • add DiversityMetric.approx_geomean_separation() based on fast_log2 and fast_exp2
    • add benchmarking of different DiversityMetric implementations to CLI & docs

  • improved:

    • further improve test coverage of fast_log* & fast_exp* functions
    • make DiversityMetric implementation more flexible

v0.2.1

(2025-12-02)

  • new:

    • fast_exp* functions in the same spirit as the updated fast_log* functions

  • improved:

    • fast_log_* & fast_log2_*
      • simplify & speedup by only supporting degree 2 (we don't need more accuracy)
      • update computation of coefficients to guarantee continuity and smoothness of the approximation

v0.2.0

(2025-11-30)

  • new:
    • initial MaxDivSolver framework

v0.1.3

(2025-11-27)

  • improved:
    • further speed up test coverage generation
    • add ReadTheDocs build status badge to README
    • add License badge to README

v0.1.2

(2025-11-25)

  • new:

    • implement randint_constrained_robust
    • remove excessively slow pure-Python randint_constrained & promote randint_constrained_numba -> randint_constrained

  • improved:

    • speed up test coverage generation
    • improve visual clarity of randint_constrained benchmarking output
    • add benchmarking for randint_constrained_robust to benchmark randint_constrained CLI command

v0.1.1

(2025-11-23)

  • new:
    • implement Constraints class
    • implement sampling.con.randint_constrained_numba function, offering a 10-100x speedup over the non-numba version

v0.1.0

(2025-11-22)

  • new:

    • add benchmark randint_constrained command to CLI

  • improved:

    • various minor improvements to docs

  • internal:

    • improve test coverage of cli functionality

v0.0.9

(2025-11-21)

  • new:

    • add sampling.con.randint_constrained, which is like randint but supporting constraints

  • improved:

    • make naming more consistent with other known functionality: sampling.discrete.sample_int -> sampling.uncon.randint
    • ensure randint also works with non-normalized probabilities (with minimal loss of efficiency, especially in numba-flavor)

  • internal:

    • update local dev tooling to declutter project root

v0.0.8

(2025-11-18)

  • improved:
    • sampling.discrete.sample_int: speedup for non-uniform sampling without replacement (custom min-heap implementation)

v0.0.7

(2025-11-17)

  • improved:

    • test coverage of internal rng functionality
    • improve speed of generating int32 arrays in sampling.discrete.sample_int

  • internal:

    • add pytest-xdist and pytest-rerunfailures to improve test performance & robustness

v0.0.6

(2025-11-16)

  • new:

    • make numba and non-numba versions of sampling.discrete.sample_int public and self-sustained (full-featured)
    • CLI command numba-status to report on numba installation and configuration status

  • improved:

    • sampling.discrete.sample_int:
      • significant speedup
      • improve benchmarking + add notes on optimization efforts
    • improve coverage statistics for numba-decorated functions
    • make numba a non-optional dependency for the sake of code simplicity
    • improve granularity of speed-vs-accuracy trade-off in benchmarking functionality (add --speed argument)

v0.0.5

(2025-11-08)

  • new:

    • implement max-div CLI, allowing package to be installed as tool to define system-wide command 'max-div'

  • improved:

    • allow benchmarking functionality to output results in Markdown format
    • switch docs to use readthedocs theme

v0.0.4

(2025-11-08)

  • improved:
    • further improvements to CI/CD pipeline

v0.0.3

(2025-11-07)

  • new:
    • move to trunk-based development workflow with release branches

v0.0.2

(2025-11-02)

  • new:
    • sampling.discrete.sample_int
    • benchmark.benchmark_sample_int

v0.0.1

(2025-11-01)

  • Initial project setup & framework