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refactor: reorganize optimizers into subfolders and migrate to uv (#30)
* refactor: reorganize optimizers into categorical subfolders * refactor: reorganize optimizers into categorical subfolders - Move 54 optimizer files into 7 categorical subfolders: - gradient_based/ (11): AdaDelta, AdaGrad, Adam, etc. - swarm_intelligence/ (12): PSO, ACO, Bat, Bee, etc. - evolutionary/ (6): GA, DE, CMA-ES, etc. - classical/ (9): BFGS, Nelder-Mead, etc. - metaheuristic/ (12): Harmony Search, etc. - constrained/ (2): Augmented Lagrangian, SLP - probabilistic/ (2): TPE, LDA - Add __init__.py with exports to each subfolder - Update root opt/__init__.py for backward compatibility - All existing imports still work (from opt import X)" * ci: update workflow to use uv for building * test: add comprehensive test suite for all optimizers * ci: add test and lint workflow * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix: update ruff config for Python 3.10+ and fix pre-commit * docs: update README for new folder structure and uv migration * Initial plan * fix: address review comments - fix BeeAlgorithm typo and add tests for BatAlgorithm and BeeAlgorithm Co-authored-by: Anselmoo <13209783+Anselmoo@users.noreply.github.com> --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
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‎.github/workflows/python-publish.yaml‎

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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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- name: Install uv
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uses: astral-sh/setup-uv@v4
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with:
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python-version: "3.x"
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- name: Install pypa/build
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run: >-
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python3 -m
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pip install
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build
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--user
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version: "latest"
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- name: Set up Python
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run: uv python install 3.12
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- name: Build a binary wheel and a source tarball
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run: python3 -m build
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run: uv build
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- name: Store the distribution packages
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uses: actions/upload-artifact@v4
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with:

‎.github/workflows/tests.yaml‎

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name: Tests
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on:
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push:
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branches:
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- main
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pull_request:
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branches:
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- main
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jobs:
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test:
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name: Test Python ${{ matrix.python-version }}
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.10", "3.11", "3.12"]
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steps:
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- uses: actions/checkout@v4
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- name: Install uv
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uses: astral-sh/setup-uv@v4
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with:
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version: "latest"
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- name: Set up Python ${{ matrix.python-version }}
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run: uv python install ${{ matrix.python-version }}
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- name: Install dependencies
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run: uv sync
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- name: Run tests
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run: uv run pytest opt/test/ -v --tb=short
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lint:
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name: Lint
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Install uv
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uses: astral-sh/setup-uv@v4
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with:
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version: "latest"
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- name: Set up Python
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run: uv python install 3.12
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- name: Install dependencies
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run: uv sync
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- name: Run ruff check
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run: uv run ruff check opt/
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- name: Run ruff format check
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run: uv run ruff format --check opt/

‎.pre-commit-config.yaml‎

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# See https://pre-commit.com/hooks.html for more hooks
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: "v4.5.0"
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rev: "v6.0.0"
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hooks:
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- id: trailing-whitespace
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- id: end-of-file-fixer
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- id: check-yaml
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- id: check-added-large-files
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: "v0.3.4"
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rev: "v0.14.10"
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hooks:
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- id: ruff
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language_version: python3.12
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types_or: [ python, pyi, jupyter ]
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types_or: [python, pyi, jupyter]
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- id: ruff-format
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language_version: python3.12

‎README.md‎

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‎opt/__init__.py‎

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"""Useful optimizers, a set of optimization algorithms."""
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"""Useful optimizers, a set of optimization algorithms.
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This package provides 54 optimization algorithms organized into categories:
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- gradient_based: Gradient-based optimizers (AdaDelta, AdaGrad, Adam, etc.)
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- swarm_intelligence: Nature-inspired swarm algorithms (PSO, ACO, etc.)
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- evolutionary: Evolutionary algorithms (GA, DE, CMA-ES, etc.)
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- classical: Classical optimization methods (BFGS, Nelder-Mead, etc.)
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- metaheuristic: Metaheuristic algorithms (Harmony Search, etc.)
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- constrained: Constrained optimization methods
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- probabilistic: Probabilistic optimization methods
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All optimizers are re-exported at the package level for backward compatibility.
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"""
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from __future__ import annotations
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# Base class
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from opt.abstract_optimizer import AbstractOptimizer
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# Classical algorithms
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from opt.classical import BFGS
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from opt.classical import LBFGS
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from opt.classical import ConjugateGradient
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from opt.classical import HillClimbing
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from opt.classical import NelderMead
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from opt.classical import Powell
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from opt.classical import SimulatedAnnealing
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from opt.classical import TabuSearch
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from opt.classical import TrustRegion
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# Constrained optimization
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from opt.constrained import AugmentedLagrangian
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from opt.constrained import SuccessiveLinearProgramming
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# Evolutionary algorithms
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from opt.evolutionary import CMAESAlgorithm
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from opt.evolutionary import CulturalAlgorithm
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from opt.evolutionary import DifferentialEvolution
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from opt.evolutionary import EstimationOfDistributionAlgorithm
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from opt.evolutionary import GeneticAlgorithm
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from opt.evolutionary import ImperialistCompetitiveAlgorithm
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from opt.gradient_based import SGD
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# Gradient-based algorithms
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from opt.gradient_based import ADAGrad
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from opt.gradient_based import ADAMOptimization
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from opt.gradient_based import AMSGrad
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from opt.gradient_based import AdaDelta
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from opt.gradient_based import AdaMax
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from opt.gradient_based import AdamW
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from opt.gradient_based import Nadam
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from opt.gradient_based import NesterovAcceleratedGradient
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from opt.gradient_based import RMSprop
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from opt.gradient_based import SGDMomentum
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# Metaheuristic algorithms
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from opt.metaheuristic import CollidingBodiesOptimization
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from opt.metaheuristic import CrossEntropyMethod
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from opt.metaheuristic import EagleStrategy
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from opt.metaheuristic import HarmonySearch
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from opt.metaheuristic import ParticleFilter
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from opt.metaheuristic import ShuffledFrogLeapingAlgorithm
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from opt.metaheuristic import SineCosineAlgorithm
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from opt.metaheuristic import StochasticDiffusionSearch
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from opt.metaheuristic import StochasticFractalSearch
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from opt.metaheuristic import VariableDepthSearch
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from opt.metaheuristic import VariableNeighborhoodSearch
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from opt.metaheuristic import VeryLargeScaleNeighborhood
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# Probabilistic algorithms
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from opt.probabilistic import LDAnalysis
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from opt.probabilistic import ParzenTreeEstimator
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# Swarm intelligence algorithms
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from opt.swarm_intelligence import AntColony
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from opt.swarm_intelligence import ArtificialFishSwarm
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from opt.swarm_intelligence import BatAlgorithm
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from opt.swarm_intelligence import BeeAlgorithm
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from opt.swarm_intelligence import CatSwarmOptimization
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from opt.swarm_intelligence import CuckooSearch
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from opt.swarm_intelligence import FireflyAlgorithm
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from opt.swarm_intelligence import GlowwormSwarmOptimization
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from opt.swarm_intelligence import GreyWolfOptimizer
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from opt.swarm_intelligence import ParticleSwarm
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from opt.swarm_intelligence import SquirrelSearchAlgorithm
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from opt.swarm_intelligence import WhaleOptimizationAlgorithm
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__version__ = "0.1.2"
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__all__: list[str] = [
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# Classical
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"BFGS",
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"LBFGS",
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"SGD",
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"ADAGrad",
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"ADAMOptimization",
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"AMSGrad",
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# Base class
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"AbstractOptimizer",
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# Gradient-based
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"AdaDelta",
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"AdaMax",
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"AdamW",
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# Swarm intelligence
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"AntColony",
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"ArtificialFishSwarm",
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# Constrained
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"AugmentedLagrangian",
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"BatAlgorithm",
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"BeeAlgorithm",
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# Evolutionary
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"CMAESAlgorithm",
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"CatSwarmOptimization",
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# Metaheuristic
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"CollidingBodiesOptimization",
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"ConjugateGradient",
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"CrossEntropyMethod",
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"CuckooSearch",
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"CulturalAlgorithm",
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"DifferentialEvolution",
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"EagleStrategy",
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"EstimationOfDistributionAlgorithm",
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"FireflyAlgorithm",
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"GeneticAlgorithm",
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"GlowwormSwarmOptimization",
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"GreyWolfOptimizer",
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"HarmonySearch",
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"HillClimbing",
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"ImperialistCompetitiveAlgorithm",
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# Probabilistic
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"LDAnalysis",
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"Nadam",
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"NelderMead",
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"NesterovAcceleratedGradient",
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"ParticleFilter",
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"ParticleSwarm",
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"ParzenTreeEstimator",
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"Powell",
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"RMSprop",
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"SGDMomentum",
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"ShuffledFrogLeapingAlgorithm",
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"SimulatedAnnealing",
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"SineCosineAlgorithm",
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"SquirrelSearchAlgorithm",
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"StochasticDiffusionSearch",
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"StochasticFractalSearch",
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"SuccessiveLinearProgramming",
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"TabuSearch",
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"TrustRegion",
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"VariableDepthSearch",
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"VariableNeighborhoodSearch",
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"VeryLargeScaleNeighborhood",
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"WhaleOptimizationAlgorithm",
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]

‎opt/classical/__init__.py‎

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"""Classical optimization algorithms.
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This module contains traditional mathematical optimization methods including derivative-based
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and derivative-free approaches. Includes: BFGS, Conjugate Gradient, Hill Climbing, L-BFGS,
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Nelder-Mead, Powell, Simulated Annealing, Tabu Search, and Trust Region methods.
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"""
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from __future__ import annotations
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from opt.classical.bfgs import BFGS
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from opt.classical.conjugate_gradient import ConjugateGradient
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from opt.classical.hill_climbing import HillClimbing
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from opt.classical.lbfgs import LBFGS
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from opt.classical.nelder_mead import NelderMead
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from opt.classical.powell import Powell
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from opt.classical.simulated_annealing import SimulatedAnnealing
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from opt.classical.tabu_search import TabuSearch
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from opt.classical.trust_region import TrustRegion
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__all__: list[str] = [
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"BFGS",
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"LBFGS",
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"ConjugateGradient",
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"HillClimbing",
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"NelderMead",
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"Powell",
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"SimulatedAnnealing",
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"TabuSearch",
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"TrustRegion",
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]

opt/bfgs.py renamed to opt/classical/bfgs.py

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opt/conjugate_gradient.py renamed to opt/classical/conjugate_gradient.py

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opt/hill_climbing.py renamed to opt/classical/hill_climbing.py

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opt/lbfgs.py renamed to opt/classical/lbfgs.py

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