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Merge pull request #3140 from ChrisRackauckas-Claude/add-dde-sublibrary
Add DelayDiffEq as a sublibrary
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‎lib/DelayDiffEq/LICENSE.md‎

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The DelayDiffEq.jl package is licensed under the MIT "Expat" License:
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> Copyright (c) 2017: ChrisRackauckas.
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>
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> Permission is hereby granted, free of charge, to any person obtaining a copy
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> of this software and associated documentation files (the "Software"), to deal
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> in the Software without restriction, including without limitation the rights
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> to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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> copies of the Software, and to permit persons to whom the Software is
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> furnished to do so, subject to the following conditions:
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>
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> The above copyright notice and this permission notice shall be included in all
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> copies or substantial portions of the Software.
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>
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> THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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> IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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> AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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> LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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> OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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> SOFTWARE.
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>

‎lib/DelayDiffEq/Project.toml‎

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name = "DelayDiffEq"
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uuid = "c7a87de4-83fd-43d3-ac6b-85200c5401ec"
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authors = ["Chris Rackauckas <accounts@chrisrackauckas.com>"]
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version = "1.0.0"
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[deps]
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ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
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DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
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DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
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FastBroadcast = "7034ab61-46d4-4ed7-9d0f-46aef9175898"
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ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
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OrdinaryDiffEqCore = "bbf590c4-e513-4bbe-9b18-05decba2e5d8"
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OrdinaryDiffEqDefault = "50262376-6c5a-4cf5-baba-aaf4f84d72d7"
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OrdinaryDiffEqDifferentiation = "4302a76b-040a-498a-8c04-15b101fed76b"
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OrdinaryDiffEqFunctionMap = "d3585ca7-f5d3-4ba6-8057-292ed1abd90f"
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OrdinaryDiffEqNonlinearSolve = "127b3ac7-2247-4354-8eb6-78cf4e7c58e8"
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OrdinaryDiffEqRosenbrock = "43230ef6-c299-4910-a778-202eb28ce4ce"
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RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
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Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
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SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
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SciMLLogging = "a6db7da4-7206-11f0-1eab-35f2a5dbe1d1"
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SimpleNonlinearSolve = "727e6d20-b764-4bd8-a329-72de5adea6c7"
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SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
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[extras]
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ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
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Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
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DDEProblemLibrary = "f42792ee-6ffc-4e2a-ae83-8ee2f22de800"
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DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
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DiffEqDevTools = "f3b72e0c-5b89-59e1-b016-84e28bfd966d"
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ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
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FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
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LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
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OrdinaryDiffEqBDF = "6ad6398a-0878-4a85-9266-38940aa047c8"
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OrdinaryDiffEqFeagin = "101fe9f7-ebb6-4678-b671-3a81e7194747"
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OrdinaryDiffEqHighOrderRK = "d28bc4f8-55e1-4f49-af69-84c1a99f0f58"
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OrdinaryDiffEqLowOrderRK = "1344f307-1e59-4825-a18e-ace9aa3fa4c6"
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OrdinaryDiffEqLowStorageRK = "b0944070-b475-4768-8dec-fb6eb410534d"
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OrdinaryDiffEqSDIRK = "2d112036-d095-4a1e-ab9a-08536f3ecdbf"
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OrdinaryDiffEqSSPRK = "669c94d9-1f4b-4b64-b377-1aa079aa2388"
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OrdinaryDiffEqTsit5 = "b1df2697-797e-41e3-8120-5422d3b24e4a"
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OrdinaryDiffEqVerner = "79d7bb75-1356-48c1-b8c0-6832512096c2"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
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SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
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[compat]
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ADTypes = "1.16"
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Aqua = "0.8.11"
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ArrayInterface = "7"
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DDEProblemLibrary = "0.1"
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DataStructures = "0.18, 0.19"
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DiffEqBase = "6.209"
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DiffEqCallbacks = "4"
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DiffEqDevTools = "2.44.4"
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ExplicitImports = "1.13.1"
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FastBroadcast = "0.3.5"
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FiniteDiff = "2.27"
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ForwardDiff = "0.10.38, 1"
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LinearAlgebra = "1"
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LinearSolve = "3.54"
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Logging = "1"
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OrdinaryDiffEqBDF = "1.16.0"
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OrdinaryDiffEqCore = "3"
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OrdinaryDiffEqDefault = "1.9"
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OrdinaryDiffEqDifferentiation = "2.2.1"
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OrdinaryDiffEqFeagin = "1.3.0"
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OrdinaryDiffEqFunctionMap = "1.6"
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OrdinaryDiffEqHighOrderRK = "1.4.0"
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OrdinaryDiffEqLowOrderRK = "1.5.0"
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OrdinaryDiffEqLowStorageRK = "1.12.0"
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OrdinaryDiffEqNonlinearSolve = "1.16"
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OrdinaryDiffEqRosenbrock = "1.19"
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OrdinaryDiffEqSDIRK = "1.6.0"
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OrdinaryDiffEqSSPRK = "1.5.0"
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OrdinaryDiffEqTsit5 = "1.4.0"
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OrdinaryDiffEqVerner = "1.5.0"
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Random = "<0.0.1, 1"
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RecursiveArrayTools = "3"
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Reexport = "1.0"
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SafeTestsets = "0.1.0"
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SciMLBase = "2.115.0"
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SciMLLogging = "1.4.0"
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SimpleNonlinearSolve = "2"
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SymbolicIndexingInterface = "0.3.36"
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Test = "<0.0.1, 1"
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Unitful = "1"
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julia = "1.10"
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[targets]
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test = ["ADTypes", "Aqua", "DDEProblemLibrary", "DiffEqCallbacks", "DiffEqDevTools", "ExplicitImports", "FiniteDiff", "LinearSolve", "OrdinaryDiffEqBDF", "OrdinaryDiffEqFeagin", "OrdinaryDiffEqHighOrderRK", "OrdinaryDiffEqLowOrderRK", "OrdinaryDiffEqLowStorageRK", "OrdinaryDiffEqSDIRK", "OrdinaryDiffEqSSPRK", "OrdinaryDiffEqTsit5", "OrdinaryDiffEqVerner", "Random", "RecursiveArrayTools", "SafeTestsets", "Test", "Unitful"]
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[sources.OrdinaryDiffEqCore]
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path = "../OrdinaryDiffEqCore"
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[sources.OrdinaryDiffEqDefault]
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path = "../OrdinaryDiffEqDefault"
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[sources.OrdinaryDiffEqDifferentiation]
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path = "../OrdinaryDiffEqDifferentiation"
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[sources.OrdinaryDiffEqFunctionMap]
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path = "../OrdinaryDiffEqFunctionMap"
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[sources.OrdinaryDiffEqNonlinearSolve]
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path = "../OrdinaryDiffEqNonlinearSolve"
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[sources.OrdinaryDiffEqRosenbrock]
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path = "../OrdinaryDiffEqRosenbrock"
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[sources.OrdinaryDiffEqBDF]
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path = "../OrdinaryDiffEqBDF"
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[sources.OrdinaryDiffEqFeagin]
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path = "../OrdinaryDiffEqFeagin"
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[sources.OrdinaryDiffEqHighOrderRK]
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path = "../OrdinaryDiffEqHighOrderRK"
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[sources.OrdinaryDiffEqLowOrderRK]
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path = "../OrdinaryDiffEqLowOrderRK"
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[sources.OrdinaryDiffEqLowStorageRK]
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path = "../OrdinaryDiffEqLowStorageRK"
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[sources.OrdinaryDiffEqSDIRK]
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path = "../OrdinaryDiffEqSDIRK"
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[sources.OrdinaryDiffEqSSPRK]
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path = "../OrdinaryDiffEqSSPRK"
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[sources.OrdinaryDiffEqTsit5]
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path = "../OrdinaryDiffEqTsit5"
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[sources.OrdinaryDiffEqVerner]
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path = "../OrdinaryDiffEqVerner"

‎lib/DelayDiffEq/src/DelayDiffEq.jl‎

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module DelayDiffEq
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import Reexport: @reexport, Reexport
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@reexport using SciMLBase
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import OrdinaryDiffEqCore, OrdinaryDiffEqNonlinearSolve, OrdinaryDiffEqDifferentiation,
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OrdinaryDiffEqRosenbrock
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import OrdinaryDiffEqDefault: OrdinaryDiffEqDefault
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import OrdinaryDiffEqFunctionMap: OrdinaryDiffEqFunctionMap
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using DataStructures: BinaryMinHeap
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using LinearAlgebra: opnorm, I
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using Logging: @logmsg
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using RecursiveArrayTools: copyat_or_push!, recursivecopy, recursivecopy!,
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recursive_bottom_eltype, recursive_unitless_bottom_eltype,
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recursive_unitless_eltype
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using ForwardDiff: ForwardDiff
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import ArrayInterface
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import SimpleNonlinearSolve
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import SymbolicIndexingInterface as SII
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using SciMLBase: AbstractDDEAlgorithm, AbstractDDEIntegrator, AbstractODEIntegrator,
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DEIntegrator
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using Base: deleteat!
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import FastBroadcast: @..
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using OrdinaryDiffEqNonlinearSolve: NLAnderson, NLFunctional
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using OrdinaryDiffEqCore: AbstractNLSolverCache, SlowConvergence,
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alg_extrapolates, alg_maximum_order, initialize!, DEVerbosity
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using OrdinaryDiffEqRosenbrock: RosenbrockMutableCache
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using OrdinaryDiffEqFunctionMap: FunctionMap
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# using OrdinaryDiffEqDifferentiation: resize_grad_config!, resize_jac_config!
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# Explicit imports for functions
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using OrdinaryDiffEqCore: AutoSwitch, CompositeAlgorithm
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using OrdinaryDiffEqDefault: DefaultODEAlgorithm
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using SciMLBase: CallbackSet, DAEProblem, DDEProblem, DESolution, ODEProblem, ReturnCode,
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VectorContinuousCallback, addat!, addat_non_user_cache!,
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deleteat_non_user_cache!,
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du_cache, full_cache, get_tmp_cache, isinplace,
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reeval_internals_due_to_modification!,
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reinit!, resize_non_user_cache!, savevalues!, u_cache, user_cache,
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step!, terminate!, u_modified!, get_proposed_dt, set_proposed_dt!,
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auto_dt_reset!,
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add_tstop!, add_saveat!, get_du, get_du!, addsteps!,
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change_t_via_interpolation!, isadaptive
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using DiffEqBase: initialize!
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import DiffEqBase
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using SciMLLogging: AbstractVerbosityPreset, None, @SciMLMessage
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import SciMLBase
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export Discontinuity, MethodOfSteps
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include("discontinuity_type.jl")
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include("functionwrapper.jl")
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include("integrators/type.jl")
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include("integrators/utils.jl")
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include("integrators/interface.jl")
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include("fpsolve/type.jl")
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include("fpsolve/fpsolve.jl")
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include("fpsolve/utils.jl")
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include("fpsolve/functional.jl")
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include("cache_utils.jl")
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include("interpolants.jl")
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include("history_function.jl")
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include("algorithms.jl")
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include("track.jl")
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include("alg_utils.jl")
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include("solve.jl")
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include("utils.jl")
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# Default solver for DDEProblems
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function SciMLBase.__solve(prob::DDEProblem; kwargs...)
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return SciMLBase.solve(prob, MethodOfSteps(DefaultODEAlgorithm()); kwargs...)
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end
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function SciMLBase.__init(prob::DDEProblem; kwargs...)
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return DiffEqBase.init(prob, MethodOfSteps(DefaultODEAlgorithm()); kwargs...)
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end
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end # module

‎lib/DelayDiffEq/src/alg_utils.jl‎

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## SciMLBase Trait Definitions
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function SciMLBase.isautodifferentiable(alg::AbstractMethodOfStepsAlgorithm)
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return SciMLBase.isautodifferentiable(alg.alg)
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end
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function SciMLBase.allows_arbitrary_number_types(alg::AbstractMethodOfStepsAlgorithm)
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return SciMLBase.allows_arbitrary_number_types(alg.alg)
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end
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function SciMLBase.allowscomplex(alg::AbstractMethodOfStepsAlgorithm)
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return SciMLBase.allowscomplex(alg.alg)
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end
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SciMLBase.isdiscrete(alg::AbstractMethodOfStepsAlgorithm) = SciMLBase.isdiscrete(alg.alg)
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SciMLBase.isadaptive(alg::AbstractMethodOfStepsAlgorithm) = SciMLBase.isadaptive(alg.alg)
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## DelayDiffEq Internal Traits
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function isconstrained(alg::AbstractMethodOfStepsAlgorithm{constrained}) where {constrained}
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return constrained
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end
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OrdinaryDiffEqCore.uses_uprev(alg::AbstractMethodOfStepsAlgorithm, adaptive) = true
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function OrdinaryDiffEqCore.isimplicit(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.isimplicit(alg.alg)
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end
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function OrdinaryDiffEqCore.isdtchangeable(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.isdtchangeable(alg.alg)
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end
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function OrdinaryDiffEqCore.ismultistep(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.ismultistep(alg.alg)
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end
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function OrdinaryDiffEqCore.isautoswitch(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.isautoswitch(alg.alg)
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end
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function OrdinaryDiffEqCore.get_chunksize(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.get_chunksize(alg.alg)
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end
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function OrdinaryDiffEqCore.get_chunksize_int(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.get_chunksize_int(alg.alg)
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end
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function OrdinaryDiffEqCore.alg_autodiff(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.alg_autodiff(alg.alg)
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end
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function OrdinaryDiffEqCore.alg_difftype(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.alg_difftype(alg.alg)
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end
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function OrdinaryDiffEqCore.standardtag(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.standardtag(alg.alg)
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end
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function OrdinaryDiffEqCore.concrete_jac(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.concrete_jac(alg.alg)
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end
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function OrdinaryDiffEqCore.alg_extrapolates(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.alg_extrapolates(alg.alg)
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end
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function SciMLBase.alg_order(alg::AbstractMethodOfStepsAlgorithm)
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return SciMLBase.alg_order(alg.alg)
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end
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function OrdinaryDiffEqCore.alg_maximum_order(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.alg_maximum_order(alg.alg)
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end
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function OrdinaryDiffEqCore.alg_adaptive_order(alg::AbstractMethodOfStepsAlgorithm)
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return OrdinaryDiffEqCore.alg_adaptive_order(alg.alg)
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end
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"""
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iscomposite(alg)
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Return if algorithm `alg` is a composite algorithm.
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"""
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iscomposite(alg) = false
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iscomposite(::OrdinaryDiffEqCore.OrdinaryDiffEqCompositeAlgorithm) = true
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iscomposite(alg::AbstractMethodOfStepsAlgorithm) = iscomposite(alg.alg)
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function DiffEqBase.prepare_alg(alg::MethodOfSteps, u0, p, prob)
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return MethodOfSteps(
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DiffEqBase.prepare_alg(alg.alg, u0, p, prob);
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constrained = isconstrained(alg),
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fpsolve = alg.fpsolve
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)
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end

‎lib/DelayDiffEq/src/algorithms.jl‎

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abstract type DelayDiffEqAlgorithm <: AbstractDDEAlgorithm end
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abstract type AbstractMethodOfStepsAlgorithm{constrained} <: DelayDiffEqAlgorithm end
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struct MethodOfSteps{algType, F, constrained} <: AbstractMethodOfStepsAlgorithm{constrained}
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alg::algType
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fpsolve::F
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end
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"""
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MethodOfSteps(alg; constrained = false, fpsolve = NLFunctional())
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Construct an algorithm that solves delay differential equations by the method of steps,
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where `alg` is an ODE algorithm from OrdinaryDiffEq.jl upon which the calculation of
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steps is based.
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If the algorithm is `constrained` only steps of size at most the minimal delay will be
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taken. If it is unconstrained, fixed-point iteration `fpsolve` is applied for step sizes
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that exceed the minimal delay.
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Citations:
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General Approach
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Zivari-Piran, Hossein, and Wayne H. Enright. "An efficient unified approach for the numerical
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solution of delay differential equations." Numerical Algorithms 53.2-3 (2010): 397-417.
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State-Dependent Delays
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S. P. Corwin, D. Sarafyan and S. Thompson in "DKLAG6: a code based on continuously embedded
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sixth-order Runge-Kutta methods for the solution of state-dependent functional differential
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equations", Applied Numerical Mathematics, 1997.
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"""
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function MethodOfSteps(alg; constrained = false, fpsolve = NLFunctional())
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return MethodOfSteps{typeof(alg), typeof(fpsolve), constrained}(alg, fpsolve)
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end

‎lib/DelayDiffEq/src/cache_utils.jl‎

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function SciMLBase.unwrap_cache(integrator::DDEIntegrator, is_stiff)
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alg = integrator.alg
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cache = integrator.cache
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iscomp = alg isa CompositeAlgorithm
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if !iscomp
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return cache
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elseif alg.choice_function isa AutoSwitch
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num = is_stiff ? 2 : 1
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return get_current_cache(cache, num)
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else
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return get_current_cache(cache, integrator.cache.current)
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end
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end

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