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Merge branch 'master' into careful-event-affects
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Project.toml

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name = "ModelingToolkit"
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uuid = "961ee093-0014-501f-94e3-6117800e7a78"
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authors = ["Yingbo Ma <[email protected]>", "Chris Rackauckas <[email protected]> and contributors"]
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version = "9.54.0"
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version = "9.56.0"
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[deps]
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AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
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RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
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Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
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RuntimeGeneratedFunctions = "7e49a35a-f44a-4d26-94aa-eba1b4ca6b47"
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SCCNonlinearSolve = "9dfe8606-65a1-4bb3-9748-cb89d1561431"
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SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
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SciMLStructures = "53ae85a6-f571-4167-b2af-e1d143709226"
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Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
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OffsetArrays = "1"
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OrderedCollections = "1"
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OrdinaryDiffEq = "6.82.0"
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OrdinaryDiffEqCore = "1.7.0"
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OrdinaryDiffEqCore = "1.13.0"
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OrdinaryDiffEqNonlinearSolve = "1.3.0"
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PrecompileTools = "1"
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REPL = "1"
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RecursiveArrayTools = "3.26"
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Reexport = "0.2, 1"
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RuntimeGeneratedFunctions = "0.5.9"
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SciMLBase = "2.64"
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SCCNonlinearSolve = "1.0.0"
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SciMLBase = "2.66"
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SciMLStructures = "1.0"
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Serialization = "1"
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Setfield = "0.7, 0.8, 1"
@@ -160,6 +163,7 @@ OptimizationMOI = "fd9f6733-72f4-499f-8506-86b2bdd0dea1"
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OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
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OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
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OrdinaryDiffEqCore = "bbf590c4-e513-4bbe-9b18-05decba2e5d8"
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OrdinaryDiffEqNonlinearSolve = "127b3ac7-2247-4354-8eb6-78cf4e7c58e8"
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Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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REPL = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -174,4 +178,4 @@ Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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[targets]
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test = ["AmplNLWriter", "BenchmarkTools", "ControlSystemsBase", "DataInterpolations", "DelayDiffEq", "NonlinearSolve", "ForwardDiff", "Ipopt", "Ipopt_jll", "ModelingToolkitStandardLibrary", "Optimization", "OptimizationOptimJL", "OptimizationMOI", "OrdinaryDiffEq", "OrdinaryDiffEqCore", "REPL", "Random", "ReferenceTests", "SafeTestsets", "StableRNGs", "Statistics", "SteadyStateDiffEq", "Test", "StochasticDiffEq", "Sundials", "StochasticDelayDiffEq", "Pkg", "JET"]
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test = ["AmplNLWriter", "BenchmarkTools", "ControlSystemsBase", "DataInterpolations", "DelayDiffEq", "NonlinearSolve", "ForwardDiff", "Ipopt", "Ipopt_jll", "ModelingToolkitStandardLibrary", "Optimization", "OptimizationOptimJL", "OptimizationMOI", "OrdinaryDiffEq", "OrdinaryDiffEqCore", "REPL", "Random", "ReferenceTests", "SafeTestsets", "StableRNGs", "Statistics", "SteadyStateDiffEq", "Test", "StochasticDiffEq", "Sundials", "StochasticDelayDiffEq", "Pkg", "JET", "OrdinaryDiffEqNonlinearSolve"]

docs/src/examples/modelingtoolkitize_index_reduction.md

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\end{aligned}
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```
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These equations can be derived using the [Lagrangian equation of the first kind.](https://en.wikipedia.org/wiki/Lagrangian_mechanics#Lagrangian)
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Specifically, for a pendulum with unit mass and length $L$, which thus has
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kinetic energy $\frac{1}{2}(v_x^2 + v_y^2)$,
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potential energy $gy$,
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and holonomic constraint $x^2 + y^2 - L^2 = 0$.
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The Lagrange multiplier related to this constraint is equal to half of $T$,
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and represents the tension in the rope of the pendulum.
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As a good DifferentialEquations.jl user, one would follow
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[the mass matrix DAE tutorial](https://docs.sciml.ai/DiffEqDocs/stable/tutorials/dae_example/#Mass-Matrix-Differential-Algebraic-Equations-(DAEs))
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to arrive at code for simulating the model:

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