support basic least square for dynamic satisfaction

.gitignore

@@ -25,3 +25,6 @@ Manifest.toml
 
 # VSCode
 .vscode/
+
+# random test of some new things
+trial/

Project.toml

@@ -1,7 +1,7 @@
 name = "Interesso"
 uuid = "6fc4ee9f-0a6c-4ef5-81c0-9afe0a8161a6"
-authors = ["astroEduardo <72969764+astroEduardo@users.noreply.github.com> and contributors"]
 version = "0.1.0"
+authors = ["astroEduardo <72969764+astroEduardo@users.noreply.github.com> and contributors"]
 
 [deps]
 DynOptInterface = "6c38235a-427b-4736-80fa-cf75909744ec"
@@ -10,9 +10,15 @@ Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 
+[weakdeps]
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+
+[extensions]
+InteressoPlots = "Plots"
+
 [compat]
-MathOptInterface = "1.31"
-julia = "1.6"
+MathOptInterface = ">=1.38"
+julia = ">=1.6"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

README.md

@@ -1,7 +1,7 @@
 [//]: Logo
 <p align="center">
 <img
-    src="./docs/src/assets/logo256px.svg"
+    src="./docs/src/assets/logo.svg"
     width=256px
     >
 </p>

example/aly_chan.jl

@@ -0,0 +1,69 @@
+function aly_chan(
+    model::Interesso.Optimizer;
+    starts::Interesso.WSS=Interesso.WSS{DOI.AbstractDynamicSolution}()
+)
+
+    MOI.empty!(model)
+
+    ## Time as a phase
+    t = DOI.add_phase(model)
+    MOI.add_constraint(model, DOI.Initial(t), MOI.EqualTo(0.0))
+    MOI.add_constraint(model, DOI.Final(t), MOI.EqualTo(π/2))
+
+    ## Input Dynamic Variable
+    @variable(model, u, t)
+    MOI.add_constraint(model, u, MOI.Interval(-1.0, 1.0))
+
+    ## State Dynamic Variables
+    @variable(model, x, t)
+    @variable(model, v, t)
+    @variable(model, cost, t)
+
+    ## Boundary Conditions
+    MOI.add_constraint(model, DOI.Initial(x), MOI.EqualTo(0.0))
+    MOI.add_constraint(model, DOI.Initial(v), MOI.EqualTo(1.0))
+    MOI.add_constraint(model, DOI.Initial(cost), MOI.EqualTo(0.0))
+
+    # # Starts
+
+    ## Differential Equations
+
+    MOI.add_constraint(
+        model,
+        DOI.ExplicitDifferentialFunction(
+            v,
+            NDF(:+, Any[u], t),            
+        ),
+        MOI.EqualTo(0.0),
+    )
+
+    MOI.add_constraint(
+        model,
+        DOI.ExplicitDifferentialFunction(
+            x,
+            NDF(:+, Any[v], t),            
+        ),
+        MOI.EqualTo(0.0),
+    )
+
+    MOI.add_constraint(
+        model,
+        DOI.ExplicitDifferentialFunction(
+            cost,
+            NDF(:-, [
+                    NDF(:*, [0.5, NDF(:^, [v, 2.0], t)], t),
+                    NDF(:*, [0.5, NDF(:^, [x, 2.0], t)], t),
+                ], t)           
+        ),
+        MOI.EqualTo(0.0),
+    )
+
+    ## Objective Function
+    MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
+    obj_fun = DOI.NonlinearBoundaryFunction(:+, [DOI.Final(cost)])
+    MOI.set(model, MOI.ObjectiveFunction{typeof(obj_fun)}(), obj_fun)
+
+    Interesso.warmstart!(model, starts)
+
+    return nothing
+end