Fix update_weekly: resolve all six unsatisfiable benchmark folders + run update job on Julia 1.12

.github/workflows/update.yml

@@ -14,7 +14,7 @@ jobs:
       - name: update step
         uses: julia-actions/setup-julia@v3
         with:
-          version: '1.10'
+          version: '1.12'
       - name: Check for updates and create a PR
         run: |
               git config --local user.email "$(git log --format='%ae' HEAD^!)"

.typos.toml

@@ -13,4 +13,14 @@ ND = "ND"
 nd = "nd"
 ned = "ned"
 sitl = "sitl"
-IIF = "IIF"
+IIF = "IIF"
+opf = "opf"
+OPF = "OPF"
+MEK = "MEK"
+ein = "ein"
+pn = "pn"
+Pn = "Pn"
+dbe = "dbe"
+
+[files]
+extend-exclude = ["benchmarks/OptimizationFrameworks/opf_data/"]

benchmarks/BayesianInference/Project.toml

@@ -20,10 +20,10 @@ DiffEqBayes = "3.0.1"
 DiffEqCallbacks = "4"
 Distributions = "0.25"
 DynamicHMC = "3.1"
-OrdinaryDiffEq = "7"
+OrdinaryDiffEq = "6"
 ParameterizedFunctions = "5.3"
 Plots = "1.4"
-RecursiveArrayTools = "4"
+RecursiveArrayTools = "3"
 SciMLBenchmarks = "0.1"
 StanSample = "7"
 StaticArrays = "1"

benchmarks/Bio/Project.toml

@@ -46,7 +46,7 @@ OrdinaryDiffEqSDIRK = "2"
 OrdinaryDiffEqStabilizedRK = "2"
 OrdinaryDiffEqVerner = "2"
 Plots = "1.10"
-ReactionNetworkImporters = "0.15"
+ReactionNetworkImporters = "1"
 RecursiveFactorization = "0.2.23"
 SciMLBenchmarks = "0.1"
 Sundials = "6"

benchmarks/DynamicalODE/Henon-Heiles_energy_conservation_benchmark.jmd

@@ -161,7 +161,7 @@ function compare(mode = :inplace, all = true, plt = nothing; tmax = 1e2)
 end
 ```
 
-The `mode` argument choses between the in place approach
+The `mode` argument chooses between the in place approach
 and the out of place one. The `all` parameter is used to compare only the integrators that support both
 the in place and the out of place versions (we refer here only to the 6 high order methods chosen below).
 The `plt` argument can be used to overlay the results over a previous plot and the `tmax` keyword determines

benchmarks/DynamicalODE/Quadrupole_boson_Hamiltonian_energy_conservation_benchmark.jmd

@@ -165,8 +165,8 @@ function compare(mode = :inplace, all = true, plt = nothing; tmax = 1e2)
 end
 ```
 
-The `mode` argument choses between the in place approach
-and the out of place one. The `all` parameter is used to compare only the integrators that support both the in place and the out of place versions (we reffer here only to the 6 high order methods chosen bellow).
+The `mode` argument chooses between the in place approach
+and the out of place one. The `all` parameter is used to compare only the integrators that support both the in place and the out of place versions (we refer here only to the 6 high order methods chosen bellow).
 The `plt` argument can be used to overlay the results over a previous plot and the `tmax` keyword determines the simulation time.
 
 Note:

benchmarks/HybridJumps/Project.toml

@@ -38,7 +38,7 @@ LazySets = "4, 5"
 LinearSolve = "3"
 OrdinaryDiffEq = "7"
 Parameters = "0.12"
-PiecewiseDeterministicMarkovProcesses = "0.0.10"
+PiecewiseDeterministicMarkovProcesses = "0.0.12"
 Plots = "1.39"
 PyCall = "1"
 RecursiveArrayTools = "4"

benchmarks/HybridJumps/Synapse.jmd

@@ -15,7 +15,7 @@ const PDMP = PiecewiseDeterministicMarkovProcesses
 const fmt = :png
 ```
 
-This benchmark implements the stochastic model of hippocampal synaptic plasticity with geometrical readount of enzyme dinamics from Rodrigues et al. [1]. The source code for the model was obtained from the Github repository [SynapseElife](https://github.com/rveltz/SynapseElife/) that accompanies the paper. The original source code is licensed with the MIT license. We have added comments on the parts of the code that were directly borrowed from the repository.
+This benchmark implements the stochastic model of hippocampal synaptic plasticity with geometrical readount of enzyme dynamics from Rodrigues et al. [1]. The source code for the model was obtained from the Github repository [SynapseElife](https://github.com/rveltz/SynapseElife/) that accompanies the paper. The original source code is licensed with the MIT license. We have added comments on the parts of the code that were directly borrowed from the repository.
 
 Initial idea for benchmarking this model comes from a [Discourse discussion](https://discourse.julialang.org/t/help-me-beat-lsoda/88236).
 

benchmarks/Jumps/Project.toml

@@ -30,11 +30,11 @@ DataFrames = "1.3.4"
 DifferentialEquations = "8"
 Distributions = "0.25"
 Graphs = "1"
-JumpProblemLibrary = "1.0.0"
+JumpProblemLibrary = "2"
 JumpProcesses = "9.9"
 ModelingToolkit = "11"
 OrdinaryDiffEq = "7"
-PiecewiseDeterministicMarkovProcesses = "0.0.10"
+PiecewiseDeterministicMarkovProcesses = "0.0.12"
 Plots = "1.39"
 PyCall = "1.96.4"
 ReactionNetworkImporters = "1"

benchmarks/Jumps/Spatial_Signaling_Sanft.jmd

@@ -13,7 +13,7 @@ using Catalyst, JumpProcesses, BenchmarkTools, Plots, Random
 
 Here we implement the model from [^1] (8 species and 12 reactions) for different
 mesh sizes, and benchmark the performance of JumpProcesses.jl's spatial
-stochastic simulation alorithms (SSAs). Below, the value `N` will denote the
+stochastic simulation algorithms (SSAs). Below, the value `N` will denote the
 number of subvolumes along one dimension of a cubic grid, representing the
 reaction volume. In [^1] this value ranges from 20 to 60.
 

benchmarks/LinearSolve/MatrixDepot.jmd

@@ -26,7 +26,7 @@ algs = [
 algnames = ["UMFPACK", "KLU", "Pardiso", "Sparspak", "ParU"]
 algnames_transpose = reshape(algnames, 1, length(algnames))
 
-cols = [:red, :blue, :green, :magenta, :turqoise, :cyan] # one color per alg
+cols = [:red, :blue, :green, :magenta, :turquoise, :cyan] # one color per alg
 
 # matrices = ["HB/1138_bus", "HB/494_bus", "HB/662_bus", "HB/685_bus", "HB/bcsstk01", "HB/bcsstk02", "HB/bcsstk03", "HB/bcsstk04",  "HB/bcsstk05", "HB/bcsstk06", "HB/bcsstk07", "HB/bcsstk08", "HB/bcsstk09", "HB/bcsstk10", "HB/bcsstk11", "HB/bcsstk12", "HB/bcsstk13", "HB/bcsstk14", "HB/bcsstk15", "HB/bcsstk16"]
 allmatrices_md = listnames("*/*")

benchmarks/LinearSolve/SparsePDE.jmd

@@ -46,7 +46,7 @@ algs = [
     SparspakFactorization(),
     ParUFactorization()
 ]
-cols = [:red, :blue, :green, :magenta, :turqoise, :cyan] # one color per alg
+cols = [:red, :blue, :green, :magenta, :turquoise, :cyan] # one color per alg
 
 __parameterless_type(T) = Base.typename(T).wrapper
 parameterless_type(x) = __parameterless_type(typeof(x))

benchmarks/NBodySimulator/acceleration_functions.jmd

@@ -198,22 +198,22 @@ end
 function acceleration(simulation::NBodySimulation{<:WaterSPCFw})
     (u0, v0, n) = gather_bodies_initial_coordinates(simulation)
 
-    (o_acelerations, h_acelerations) = gather_accelerations_for_potentials(simulation)
+    (o_accelerations, h_accelerations) = gather_accelerations_for_potentials(simulation)
     group_accelerations = gather_group_accelerations(simulation)
     simultaneous_acceleration = gather_simultaneous_acceleration(simulation)
 
     function soode_system!(dv, v, u, p, t)
         @inbounds for i in 1:n
             a = MVector(0.0, 0.0, 0.0)
-            for acceleration! in o_acelerations
+            for acceleration! in o_accelerations
                 acceleration!(a, u, v, t, 3 * (i - 1) + 1);
             end
             dv[:, 3 * (i - 1) + 1] .= a
         end
         @inbounds for i in 1:n, j in (2, 3)
 
             a = MVector(0.0, 0.0, 0.0)
-            for acceleration! in h_acelerations
+            for acceleration! in h_accelerations
                 acceleration!(a, u, v, t, 3 * (i - 1) + j);
             end
             dv[:, 3 * (i - 1) + j] .= a

benchmarks/ParameterEstimation/Project.toml

@@ -21,13 +21,13 @@ BlackBoxOptim = "0.6"
 DiffEqParamEstim = "2"
 Distributions = "0.25"
 ForwardDiff = "0.10, 1"
-ModelingToolkit = "11"
+ModelingToolkit = "9"
 NLopt = "1"
 Optimization = "4, 5"
 OptimizationBBO = "0.4"
 OptimizationNLopt = "0.3"
-OrdinaryDiffEq = "7"
+OrdinaryDiffEq = "6"
 ParameterizedFunctions = "5.3"
 Plots = "1.4"
-RecursiveArrayTools = "4"
+RecursiveArrayTools = "3"
 SciMLBenchmarks = "0.1"