NaiveAbstraction renaming (#341)

* NaiveAbstraction -> ClassicalAbstraction

* ClassicalAbstraction -> UniformGridAbstraction

docs/src/examples/solvers/DC-DC converter.jl

@@ -1,5 +1,5 @@
 using Test     #src
-# # Example: DC-DC converter solved by [Naive abstraction](https://github.com/dionysos-dev/Dionysos.jl/blob/master/docs/src/manual/manual.md#solvers).
+# # Example: DC-DC converter solved by [Uniform grid abstraction](https://github.com/dionysos-dev/Dionysos.jl/blob/master/docs/src/manual/manual.md#solvers).
 #
 #md # [![Binder](https://mybinder.org/badge_logo.svg)](@__BINDER_ROOT_URL__/generated/DC-DC converter.ipynb)
 #md # [![nbviewer](https://img.shields.io/badge/show-nbviewer-579ACA.svg)](@__NBVIEWER_ROOT_URL__/generated/DC-DC converter.ipynb)
@@ -55,7 +55,7 @@ hu = SVector(1)
 input_grid = DO.GridFree(u0, hu)
 
 using JuMP
-optimizer = MOI.instantiate(AB.NaiveAbstraction.Optimizer)
+optimizer = MOI.instantiate(AB.UniformGridAbstraction.Optimizer)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("concrete_problem"), concrete_problem)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("state_grid"), state_grid)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("input_grid"), input_grid)

docs/src/examples/solvers/Path planning.jl

@@ -1,5 +1,5 @@
 using Test     #src
-# # Example: Path planning problem solved by [Naive abstraction](https://github.com/dionysos-dev/Dionysos.jl/blob/master/docs/src/manual/manual.md#solvers).
+# # Example: Path planning problem solved by [Uniform grid abstraction](https://github.com/dionysos-dev/Dionysos.jl/blob/master/docs/src/manual/manual.md#solvers).
 #
 #md # [![Binder](https://mybinder.org/badge_logo.svg)](@__BINDER_ROOT_URL__/generated/Path planning.ipynb)
 #md # [![nbviewer](https://img.shields.io/badge/show-nbviewer-579ACA.svg)](@__NBVIEWER_ROOT_URL__/generated/Path planning.ipynb)
@@ -65,10 +65,10 @@ u0 = SVector(0.0, 0.0);
 h = SVector(0.3, 0.3);
 input_grid = DO.GridFree(u0, h);
 
-# We now solve the optimal control problem with the `Abstraction.NaiveAbstraction.Optimizer`.
+# We now solve the optimal control problem with the `Abstraction.UniformGridAbstraction.Optimizer`.
 
 using JuMP
-optimizer = MOI.instantiate(AB.NaiveAbstraction.Optimizer)
+optimizer = MOI.instantiate(AB.UniformGridAbstraction.Optimizer)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("concrete_problem"), concrete_problem)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("state_grid"), state_grid)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("input_grid"), input_grid)

docs/src/manual/manual.md

@@ -48,7 +48,7 @@ The following tables summarize the different solvers.
 
 | Type          | Full vs partial discretization | Partition vs Cover | Shape | Local controller | Abstraction | System | Reference | 
 | :--------------- | :---------- | :---------- | :---------- | :---------- | :---------- | :---------- | :---------- |
-| [`Naive abstraction`](https://dionysos-dev.github.io/Dionysos.jl/dev/reference/Optim/#Dionysos.Optim.Abstraction.NaiveAbstraction.Optimizer) | Full | Partition | Hyperrectangle | Piece-wise constant | Non-determinisitic | Continuous-time | [`SCOTS: A Tool for the Synthesis of Symbolic Controllers`](https://dl.acm.org/doi/abs/10.1145/2883817.2883834) |
+| [`Uniform grid abstraction`](https://dionysos-dev.github.io/Dionysos.jl/dev/reference/Optim/#Dionysos.Optim.Abstraction.UniformGridAbstraction.Optimizer) | Full | Partition | Hyperrectangle | Piece-wise constant | Non-determinisitic | Continuous-time | [`SCOTS: A Tool for the Synthesis of Symbolic Controllers`](https://dl.acm.org/doi/abs/10.1145/2883817.2883834) |
 | [`Lazy abstraction`](https://dionysos-dev.github.io/Dionysos.jl/dev/reference/Optim/#Dionysos.Optim.Abstraction.LazyAbstraction.Optimizer) | Partial | Partition | Hyperrectangle | Piece-wise constant | Non-determinisitic | Continuous-time | [`Alternating simulation on hierarchical abstractions`](https://ieeexplore.ieee.org/abstract/document/9683448/?casa_token=AXyECYU9FdwAAAAA:ERfvlbkORIbfGLbDd42d2K5K9SLVOjl-8kRs9pfp7lMa4QZEv0W4VgzlP8FshBlxXQF4ZQrDuzk) |
 | [`Hierarchical abstraction`](https://dionysos-dev.github.io/Dionysos.jl/dev/reference/Optim/#Dionysos.Optim.Abstraction.HierarchicalAbstraction.Optimizer) | Partial | Partition | Hyperrectangle | Piece-wise constant | Non-determinisitic | Continuous-time | [`Alternating simulation on hierarchical abstractions`](https://ieeexplore.ieee.org/abstract/document/9683448/?casa_token=AXyECYU9FdwAAAAA:ERfvlbkORIbfGLbDd42d2K5K9SLVOjl-8kRs9pfp7lMa4QZEv0W4VgzlP8FshBlxXQF4ZQrDuzk) |
 | [`Ellipsoid abstraction`](https://dionysos-dev.github.io/Dionysos.jl/dev/reference/Optim/#Dionysos.Optim.Abstraction.EllipsoidsAbstraction.Optimizer) | Full | Cover | Ellipsoid | Piece-wise affine | Determinisitic | Discrete-time affine | [`State-feedback Abstractions for Optimal Control of Piecewise-affine Systems`](https://arxiv.org/abs/2204.00315) |
@@ -65,7 +65,7 @@ The following tables summarize the different solvers.
 **Solver interface**
 
 Each solver is defined by a module which must implement the abstract type [`AbstractOptimizer`](https://jump.dev/MathOptInterface.jl/stable/reference/models/#MathOptInterface.AbstractOptimizer) and the [`Optimize!`](https://jump.dev/MathOptInterface.jl/stable/reference/models/#MathOptInterface.optimize!) function.
-For example, for the NaiveAbstraction solver, this structure and function are defined as follows
+For example, for the UniformGridAbstraction solver, this structure and function are defined as follows
 
 ```julia
 using JuMP
@@ -123,7 +123,7 @@ concrete_system = concrete_problem.system;
 Choose the solver you wish to use
 ```julia
 using JuMP
-optimizer = MOI.instantiate(AB.NaiveAbstraction.Optimizer)
+optimizer = MOI.instantiate(AB.UniformGridAbstraction.Optimizer)
 ```
 
 Define the solver's meta-parameters

docs/src/reference/Optim.md

@@ -4,7 +4,7 @@ This folder contains all the different (abstraction-based or not) solvers that c
 
 ## Abstraction-based solvers
 ```@docs
-Dionysos.Optim.Abstraction.NaiveAbstraction.Optimizer
+Dionysos.Optim.Abstraction.UniformGridAbstraction.Optimizer
 Dionysos.Optim.Abstraction.EllipsoidsAbstraction.Optimizer
 Dionysos.Optim.Abstraction.HierarchicalAbstraction.Optimizer
 Dionysos.Optim.Abstraction.LazyAbstraction.Optimizer

src/optim/abstraction/abstraction.jl

@@ -1,6 +1,6 @@
 module Abstraction
 
-include("naive_abstraction.jl")
+include("uniform_grid_abstraction.jl")
 include("lazy_abstraction.jl")
 include("hierarchical_abstraction.jl")
 include("ellipsoids_abstraction.jl")

src/optim/abstraction/naive_abstraction.jl → src/optim/abstraction/uniform_grid_abstraction.jl

@@ -1,6 +1,6 @@
-export NaiveAbstraction
+export UniformGridAbstraction
 
-module NaiveAbstraction
+module UniformGridAbstraction
 
 import Dionysos
 const DI = Dionysos

test/optim/test_NaiveAbstraction_reachability.jl → test/optim/test_UniformGridAbstraction_reachability.jl

@@ -35,10 +35,10 @@ u0 = SVector(0.0, 0.0)
 h = SVector(0.3, 0.3)
 input_grid = DO.GridFree(u0, h)
 
-# We now solve the optimal control problem with the `Abstraction.NaiveAbstraction.Optimizer`.
+# We now solve the optimal control problem with the `Abstraction.UniformGridAbstraction.Optimizer`.
 
 using JuMP
-optimizer = MOI.instantiate(AB.NaiveAbstraction.Optimizer)
+optimizer = MOI.instantiate(AB.UniformGridAbstraction.Optimizer)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("concrete_problem"), concrete_problem)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("state_grid"), state_grid)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("input_grid"), input_grid)
@@ -50,7 +50,7 @@ abstract_problem = MOI.get(optimizer, MOI.RawOptimizerAttribute("abstract_proble
 abstract_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("abstract_controller"))
 concrete_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("concrete_controller"))
 
-@testset "NaiveAbstraction reachability" begin
+@testset "UniformGridAbstraction reachability" begin
     @test length(abstract_controller.data) == 19400 #src
 end
 

test/optim/test_NaiveAbstraction_safety.jl → test/optim/test_UniformGridAbstraction_safety.jl

@@ -29,7 +29,7 @@ hu = SVector(1)
 input_grid = DO.GridFree(u0, hu)
 
 using JuMP
-optimizer = MOI.instantiate(AB.NaiveAbstraction.Optimizer)
+optimizer = MOI.instantiate(AB.UniformGridAbstraction.Optimizer)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("concrete_problem"), concrete_problem)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("state_grid"), state_grid)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("input_grid"), input_grid)
@@ -38,7 +38,7 @@ MOI.optimize!(optimizer)
 abstract_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("abstract_controller"))
 concrete_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("concrete_controller"))
 
-@testset "NaiveAbstraction safety" begin
+@testset "UniformGridAbstraction safety" begin
     @test length(abstract_controller.data) == 893803 #src
 end
 

test/optim/unit_tests_NaiveAbstraction/test_controller.jl → test/optim/unit_tests_UniformGridAbstraction/test_controller.jl

@@ -11,7 +11,7 @@ sleep(0.1) # used for good printing
 println("Started test")
 
 @testset "Controller" begin
-    contr = AB.NaiveAbstraction.NewControllerList()
+    contr = AB.UniformGridAbstraction.NewControllerList()
 
     UT.push_new!(contr, (5, 6))
     UT.push_new!(contr, (5, 6))

test/optim/unit_tests_NaiveAbstraction/test_controllerreach.jl → test/optim/unit_tests_UniformGridAbstraction/test_controllerreach.jl

@@ -75,8 +75,8 @@ println("Started test")
         push!(targetlist, SY.get_state_by_xpos(symmodel, pos))
     end
 
-    contr = AB.NaiveAbstraction.NewControllerList()
-    AB.NaiveAbstraction.compute_controller_reach!(
+    contr = AB.UniformGridAbstraction.NewControllerList()
+    AB.UniformGridAbstraction.compute_controller_reach!(
         contr,
         symmodel.autom,
         initlist,
@@ -85,14 +85,14 @@ println("Started test")
     @test length(contr) == 412
     if VERSION >= v"1.5"
         function f(autom, initlist, targetlist)
-            contr = AB.NaiveAbstraction.NewControllerList()
+            contr = AB.UniformGridAbstraction.NewControllerList()
             initset, targetset, num_targets_unreachable, current_targets, next_targets =
-                AB.NaiveAbstraction._data(contr, autom, initlist, targetlist)
+                AB.UniformGridAbstraction._data(contr, autom, initlist, targetlist)
             # Preallocates to make sure `_compute_controller_reach` does not need to allocate
             sizehint!(contr.data, 500)
             sizehint!(current_targets, 50)
             sizehint!(next_targets, 200)
-            @allocated AB.NaiveAbstraction._compute_controller_reach!(
+            @allocated AB.UniformGridAbstraction._compute_controller_reach!(
                 contr,
                 autom,
                 initset,

test/optim/unit_tests_NaiveAbstraction/test_controllersafe.jl → test/optim/unit_tests_UniformGridAbstraction/test_controllersafe.jl

@@ -74,8 +74,13 @@ println("Started test")
         push!(safelist, SY.get_state_by_xpos(symmodel, pos))
     end
 
-    contr = AB.NaiveAbstraction.NewControllerList()
-    AB.NaiveAbstraction.compute_controller_safe!(contr, symmodel.autom, initlist, safelist)
+    contr = AB.UniformGridAbstraction.NewControllerList()
+    AB.UniformGridAbstraction.compute_controller_safe!(
+        contr,
+        symmodel.autom,
+        initlist,
+        safelist,
+    )
     @test length(contr) == 15043
 
     invlist = Int[]

test/runtests.jl

@@ -21,13 +21,13 @@ include("./symbolic/test_ellipsoidal_transitions.jl")
 
 include("./problem/test_problems.jl")
 
-include("./optim/unit_tests_NaiveAbstraction/test_controller.jl")
-include("./optim/unit_tests_NaiveAbstraction/test_controllerreach.jl")
-include("./optim/unit_tests_NaiveAbstraction/test_controllersafe.jl")
-include("./optim/unit_tests_NaiveAbstraction/test_fromcontrolsystemgrowth.jl")
-include("./optim/unit_tests_NaiveAbstraction/test_fromcontrolsystemlinearized.jl")
-include("./optim/test_NaiveAbstraction_safety.jl")
-include("./optim/test_NaiveAbstraction_reachability.jl")
+include("./optim/unit_tests_UniformGridAbstraction/test_controller.jl")
+include("./optim/unit_tests_UniformGridAbstraction/test_controllerreach.jl")
+include("./optim/unit_tests_UniformGridAbstraction/test_controllersafe.jl")
+include("./optim/unit_tests_UniformGridAbstraction/test_fromcontrolsystemgrowth.jl")
+include("./optim/unit_tests_UniformGridAbstraction/test_fromcontrolsystemlinearized.jl")
+include("./optim/test_UniformGridAbstraction_safety.jl")
+include("./optim/test_UniformGridAbstraction_reachability.jl")
 include("./optim/test_lazy_abstraction.jl")
 include("./optim/test_ellipsoids_abstraction.jl")
 include("./optim/test_lazy_ellipsoids_abstraction.jl")