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Merge pull request #839 from ChrisRackauckas-Claude/extend-api-documentation
Extend API section with additional solver libraries
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‎docs/Project.toml‎

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@@ -9,6 +9,7 @@ CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
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ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
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DAEProblemLibrary = "dfb8ca35-80a1-48ba-a605-84916a45b4f8"
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DASKR = "165a45c3-f624-5814-8e85-3bdf39a7becd"
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DASSL = "e993076c-0cfd-5d6b-a1ac-36489fdf7917"
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DDEProblemLibrary = "f42792ee-6ffc-4e2a-ae83-8ee2f22de800"
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DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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DelayDiffEq = "bcd4f6db-9728-5f36-b5f7-82caef46ccdb"
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ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
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IncompleteLU = "40713840-3770-5561-ab4c-a76e7d0d7895"
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JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
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LSODA = "7f56f5a3-f504-529b-bc02-0b1fe5e64312"
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ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
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NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
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ODEInterfaceDiffEq = "09606e27-ecf5-54fc-bb29-004bd9f985bf"
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ODEProblemLibrary = "fdc4e326-1af4-4b90-96e7-779fcce2daa5"
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Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
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OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
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SDEProblemLibrary = "c72e72a9-a271-4b2b-8966-303ed956772e"
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SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
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SciMLOperators = "c0aeaf25-5076-4817-a8d5-81caf7dfa961"
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SimpleDiffEq = "05bca326-078c-5bf0-a5bf-ce7c7982d7fd"
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SparseConnectivityTracer = "9f842d2f-2579-4b1d-911e-f412cf18a3f5"
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StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
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SteadyStateDiffEq = "9672c7b4-1e72-59bd-8a11-6ac3964bc41f"
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ComponentArrays = "0.15"
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DAEProblemLibrary = "0.1"
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DASKR = "2.9.1"
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DASSL = "2"
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DDEProblemLibrary = "0.1"
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DataFrames = "1.4"
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DelayDiffEq = "5.52"
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ForwardDiff = "0.10, 1"
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IncompleteLU = "0.2"
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JLD2 = "0.4, 0.5.1, 0.6"
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LSODA = "0.7"
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ModelingToolkit = "9, 10, 11.0"
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NonlinearSolve = "3.15, 4"
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ODEInterfaceDiffEq = "3"
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ODEProblemLibrary = "0.1, 1"
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Optimization = "3, 4, 5"
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OptimizationNLopt = "0.2, 0.3"
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SDEProblemLibrary = "0.1, 1"
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SciMLBase = "2.81.0"
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SciMLOperators = "0.3, 0.4, 1"
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SimpleDiffEq = "1"
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SparseConnectivityTracer = "0.6, 1"
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StaticArrays = "1"
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SteadyStateDiffEq = "2.4"

‎docs/make.jl‎

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using Documenter, DiffEqBase, SciMLBase, OrdinaryDiffEq, OrdinaryDiffEqBDF,
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OrdinaryDiffEqCore, StochasticDiffEq, DelayDiffEq, SteadyStateDiffEq, DiffEqCallbacks
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OrdinaryDiffEqCore, StochasticDiffEq, DelayDiffEq, SteadyStateDiffEq, DiffEqCallbacks,
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BoundaryValueDiffEq
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import ODEProblemLibrary,
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SDEProblemLibrary, DDEProblemLibrary, DAEProblemLibrary, BVProblemLibrary
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using Sundials, DASKR
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using Sundials, DASKR, LSODA, DASSL, SimpleDiffEq, ODEInterfaceDiffEq
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# Use development versions for API documentation
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import Pkg
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DelayDiffEq,
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SteadyStateDiffEq,
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DiffEqCallbacks,
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Sundials, DASKR,
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BoundaryValueDiffEq,
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Sundials,
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DASKR,
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LSODA,
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DASSL,
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SimpleDiffEq,
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ODEInterfaceDiffEq,
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],
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remotes = nothing,
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linkcheck = true,

‎docs/pages.jl‎

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"features/low_dep.md",
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"features/progress_bar.md",
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],
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"External Solver APIs" => Any[
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"api/sundials.md",
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"api/daskr.md",
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"Solver Package APIs" => Any[
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"Native Julia Solvers" => Any[
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"api/delaydiffeq.md",
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"api/steadystatediffeq.md",
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"api/boundaryvaluediffeq.md",
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"api/diffeqcallbacks.md",
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"api/simplediffeq.md",
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"api/dassl.md",
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],
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"External Wrappers" => Any[
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"api/sundials.md",
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"api/daskr.md",
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"api/lsoda.md",
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"api/odeinterfacediffeq.md",
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],
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],
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"OrdinaryDiffEq.jl API" => ordinary_diffeq_pages,
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"StochasticDiffEq.jl API" => stochastic_diffeq_pages,

‎docs/src/api/boundaryvaluediffeq.md‎

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# [BoundaryValueDiffEq.jl](@id boundaryvaluediffeq_api)
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BoundaryValueDiffEq.jl is the native Julia package for solving boundary value problems
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(BVPs) within the SciML ecosystem. It provides shooting methods, MIRK collocation methods,
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FIRK methods, and more.
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## Installation
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BoundaryValueDiffEq.jl is included with DifferentialEquations.jl. To use it standalone:
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```julia
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using Pkg
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Pkg.add("BoundaryValueDiffEq")
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import BoundaryValueDiffEq
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```
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## Shooting Method APIs
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```@docs
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BoundaryValueDiffEq.Shooting
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BoundaryValueDiffEq.MultipleShooting
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```
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## MIRK Method APIs
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```@docs
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BoundaryValueDiffEq.MIRK2
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BoundaryValueDiffEq.MIRK3
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BoundaryValueDiffEq.MIRK4
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BoundaryValueDiffEq.MIRK5
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BoundaryValueDiffEq.MIRK6
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```
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## FIRK Method APIs (Lobatto)
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```@docs
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BoundaryValueDiffEq.LobattoIIIa2
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BoundaryValueDiffEq.LobattoIIIa3
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BoundaryValueDiffEq.LobattoIIIa4
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BoundaryValueDiffEq.LobattoIIIa5
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BoundaryValueDiffEq.LobattoIIIb2
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BoundaryValueDiffEq.LobattoIIIb3
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BoundaryValueDiffEq.LobattoIIIb4
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BoundaryValueDiffEq.LobattoIIIb5
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BoundaryValueDiffEq.LobattoIIIc2
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BoundaryValueDiffEq.LobattoIIIc3
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BoundaryValueDiffEq.LobattoIIIc4
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BoundaryValueDiffEq.LobattoIIIc5
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```
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## FIRK Method APIs (Radau)
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```@docs
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BoundaryValueDiffEq.RadauIIa1
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BoundaryValueDiffEq.RadauIIa2
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BoundaryValueDiffEq.RadauIIa3
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BoundaryValueDiffEq.RadauIIa5
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BoundaryValueDiffEq.RadauIIa7
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```
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## Ascher Collocation Method APIs
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```@docs
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BoundaryValueDiffEq.Ascher1
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BoundaryValueDiffEq.Ascher2
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BoundaryValueDiffEq.Ascher3
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BoundaryValueDiffEq.Ascher4
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BoundaryValueDiffEq.Ascher5
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BoundaryValueDiffEq.Ascher6
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BoundaryValueDiffEq.Ascher7
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```
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## MIRKN Method APIs
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```@docs
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BoundaryValueDiffEq.MIRKN4
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BoundaryValueDiffEq.MIRKN6
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```

‎docs/src/api/dassl.md‎

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# [DASSL.jl](@id dassl_api)
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DASSL.jl is a native Julia implementation of the DASSL algorithm for solving
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differential-algebraic equations (DAEs) within the SciML interface.
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Note that this package is not automatically included with DifferentialEquations.jl.
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To use the following algorithms, you must install and use DASSL.jl:
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```julia
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using Pkg
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Pkg.add("DASSL")
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import DASSL
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```
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These methods can be used independently of the rest of DifferentialEquations.jl.
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## DAE Solvers
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The following DAE solver is available:
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- ``dassl`` - A native Julia implementation of the DASSL algorithm. This is an
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adaptive-order, adaptive-stepsize backward differentiation formula (BDF)
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method for solving stiff systems of differential-algebraic equations.
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### Example Usage
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```julia
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using DASSL, DiffEqBase
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# Define the residual function for the DAE
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function f!(res, du, u, p, t)
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res[1] = du[1] - u[2]
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res[2] = u[1] + u[2] - 1.0
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end
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# Initial conditions
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u0 = [1.0, 0.0]
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du0 = [0.0, 1.0]
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tspan = (0.0, 10.0)
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# Create and solve the problem
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prob = DAEProblem(f!, du0, u0, tspan)
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sol = solve(prob, dassl())
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```

‎docs/src/api/delaydiffeq.md‎

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# [DelayDiffEq.jl](@id delaydiffeq_api)
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DelayDiffEq.jl is the native Julia package for solving delay differential equations
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(DDEs) within the SciML ecosystem. It provides methods for constant and state-dependent
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delays using the method of steps.
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## Installation
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DelayDiffEq.jl is included with DifferentialEquations.jl. To use it standalone:
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```julia
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using Pkg
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Pkg.add("DelayDiffEq")
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import DelayDiffEq
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```
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## DDE Solver API
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```@docs
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DelayDiffEq.MethodOfSteps
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```

‎docs/src/api/diffeqcallbacks.md‎

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# [DiffEqCallbacks.jl](@id diffeqcallbacks_api)
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DiffEqCallbacks.jl provides a library of pre-built callbacks for use with the
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SciML differential equation solvers. These include saving callbacks, manifold
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projection, domain constraints, and more.
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## Installation
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DiffEqCallbacks.jl is included with DifferentialEquations.jl. To use it standalone:
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```julia
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using Pkg
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Pkg.add("DiffEqCallbacks")
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import DiffEqCallbacks
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```
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## Callback APIs
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### Manifold Projection Callbacks
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```@docs
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DiffEqCallbacks.ManifoldProjection
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```
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### Saving Callbacks
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```@docs
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DiffEqCallbacks.SavingCallback
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DiffEqCallbacks.SavedValues
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```
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### Domain Callbacks
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```@docs
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DiffEqCallbacks.PositiveDomain
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DiffEqCallbacks.GeneralDomain
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```
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### Stepping Callbacks
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```@docs
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DiffEqCallbacks.StepsizeLimiter
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DiffEqCallbacks.FunctionCallingCallback
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```
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### Termination Callbacks
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```@docs
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DiffEqCallbacks.TerminateSteadyState
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```
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### Iterative Callbacks
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```@docs
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DiffEqCallbacks.IterativeCallback
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DiffEqCallbacks.PeriodicCallback
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```
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### Preset Time Callbacks
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```@docs
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DiffEqCallbacks.PresetTimeCallback
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```
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### AutoAbstol
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```@docs
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DiffEqCallbacks.AutoAbstol
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```

‎docs/src/api/lsoda.md‎

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# [LSODA.jl](@id lsoda_api)
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LSODA.jl is a wrapper package that provides access to the LSODA algorithm within
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the SciML interface. LSODA is a well-known method which uses automatic switching
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to solve both stiff and non-stiff equations.
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Note that this package is not automatically included with DifferentialEquations.jl.
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To use the following algorithms, you must install and use LSODA.jl:
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```julia
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using Pkg
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Pkg.add("LSODA")
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import LSODA
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```
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These methods can be used independently of the rest of DifferentialEquations.jl.
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## ODE Solver APIs
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```@docs
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LSODA.lsoda
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```

‎docs/src/api/odeinterfacediffeq.md‎

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# [ODEInterfaceDiffEq.jl](@id odeinterfacediffeq_api)
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ODEInterfaceDiffEq.jl provides a wrapper for classic Fortran ODE solvers from
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the ODEInterface.jl package into the SciML interface. These include the Hairer
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and Wanner solvers.
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Note that this package is not automatically included with DifferentialEquations.jl.
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To use the following algorithms, you must install and use ODEInterfaceDiffEq.jl:
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```julia
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using Pkg
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Pkg.add("ODEInterfaceDiffEq")
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import ODEInterfaceDiffEq
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```
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These methods can be used independently of the rest of DifferentialEquations.jl.
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## ODE Solver APIs
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```@docs
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ODEInterfaceDiffEq.dopri5
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ODEInterfaceDiffEq.dop853
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ODEInterfaceDiffEq.odex
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ODEInterfaceDiffEq.seulex
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ODEInterfaceDiffEq.radau
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ODEInterfaceDiffEq.radau5
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ODEInterfaceDiffEq.rodas
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ODEInterfaceDiffEq.ddeabm
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ODEInterfaceDiffEq.ddebdf
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```
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## BVP Solver APIs
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```@docs
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ODEInterfaceDiffEq.BVPM2
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ODEInterfaceDiffEq.BVPSOL
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ODEInterfaceDiffEq.COLNEW
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```

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