Testing Update

example/SemiInfinite/ImplicitSIP.jl

@@ -1,4 +1,4 @@
-workspace()
+#workspace()
 
 using IntervalArithmetic
 using EAGO

test/SemiInfinite/ImplicitSIP.jl

@@ -50,47 +50,75 @@ out6a = EAGO.Reform_Imp_HJ(hj,x,yrefa,Pset,ny)
 
 # later if necessary
 out5 = EAGO.Reform_Imp_HG(h,g,x,yrefa,Pset,ny,ng,eps_g)
-@test out5 == [11.0, -2.0, 9.0, 1.0, -11.0, 2.0, -9.0, -1.0, 8.5, 29.0, 10.5, 34.0]
+@test out5 == [11.0, -2.0, 9.0, 1.0, 8.5, 29.0, 10.5, 34.0]
 
 end
 
-#=
 # solves example SIP #1 with DAG contractor disabled
 @testset "SemiInfinite Explicit RHS" begin
+
+# create the SIP option object for the solver
 SIPopt1 = SIP_opts()
+
+# create solver with specified options options for lower level problem
+sep1in = EAGO_NLPSolver(LBD_func_relax = "NS-STD-OFF",  # use standard McCormick relaxations
+                        LBDsolvertype = "LP",           # use an LP problem structure for relaxed problems
+                        UBDsolvertype = "Ipopt",        # use NLP solver upper bounds (currently preferred solver)
+                        probe_depth = -1,               # disable probing
+                        variable_depth = 1000,          # use duality based range reduction to a depth of 1000 (use to high depth recommended)
+                        DAG_depth = -1,                 # don't use a DAG contractor (I need to update this for implicit SIP)
+                        STD_RR_depth = -1,              # don't use standard range reduction (problems get quite large)
+                        verbosity = "None",             # specify printing level for global optimization problem
+                        validated = true,               # use numerically validated intervals
+                        atol = 1E-7,                    # absolute tolerance (May need to play with this)
+                        rtol = 1E-5)                    # relative tolerance (May need to play with this)
+
+# create a solver for the lower/upper problems
 sep1lu = EAGO_NLPSolver(LBD_func_relax = "NS-STD-OFF",
                         LBDsolvertype = "LP",
+                        UBDsolvertype = "Ipopt",
                         probe_depth = -1,
                         variable_depth = 1000,
                         DAG_depth = -1,
-                        STD_RR_depth = 1000,
-                        ImplicitFlag = true,
-                        verbosity = "Normal",
-                        validated = true))
-sep1lu.BnBSolver.Verbosity = "None"
-sep1in = EAGO_NLPSolver(BD_func_relax = "NS-STD-OFF",
-                            LBDsolvertype = "LP",
-                            probe_depth = -1,
-                            variable_depth = 1000,
-                            DAG_depth = -1,
-                            STD_RR_depth = 1000,
-                            ImplicitFlag = true,
-                            verbosity = "Normal",
-                            validated = true))
-
-sep1in.BnBSolver.Verbosity = "None"
-SIPopt1.LLP_Opt = sep1in
-SIPopt1.LBP_Opt = sep1lu
-SIPopt1.UBP_Opt = sep1lu
-f1(x) = (1/3)*x[1]^2 + x[2]^2 + x[1]/2
-gSIP1(x,p) = (1.0-(x[1]^2)*(p[1]^2))^2 - x[1]*p[1]^2 - x[2]^2 + x[2]
-X1 = [MCInterval(-1000.0,1000.0),MCInterval(-1000.0,1000.0)]
-P1 = [MCInterval(0.0,1.0)]
-SIPoutput1 = Implicit_SIP_Solve(f1,gSIP1,X1,P1,SIPopt1)
-
-@test isapprox(SIPoutput1.LBD,0.19452787006676814,atol=1E-3)
-@test isapprox(SIPoutput1.UBD,0.19452787006676814,atol=1E-3)
+                        STD_RR_depth = -1,
+                        verbosity = "None",
+                        validated = true,
+                        atol = 1E-7,
+                        rtol = 1E-5)
+
+SIPopt1.LLP_Opt = sep1in        # Set solver for use in lower level problem
+SIPopt1.LBP_Opt = sep1lu        # Set solver for use in lower bounding problem
+SIPopt1.UBP_Opt = sep1lu        # Set solver for use in upper bounding problem
+
+SIPopt1.eps_g0 = 0.9
+SIPopt1.tol = 1E-2              # SIP tolerance
+SIPopt1.r0 = 2.0                # reduction factor for SIP routine
+SIPopt1.kmax = 5                # maximum number of iteration for SIP routine
+SIPopt1.inn_tol = 0.0           # tolerance factor usually set to tolerance of inner program
 
+# 1D Example (7.4.1 from thesis)
+# solution f = -15.8077 @ y = 2.95275
+f(x) = (x[1]-3.5)^4 - 5*(x[1]-3.5)^3 - 2*(x[1]-3.5)^2 + 15*(x[1]-3.5)
+function h(x,y,p)
+    [y[1]-(x[1]-(x[1]^3)/6+(x[1]^5)/120)/sqrt(y[1])-p[1]]
 end
-=#
+function hj(x,y,p)
+    [1.0+(x[1]-(x[1]^3)/6+(x[1]^5)/120)/(2.0*sqrt(y[1]^3))]
+end
+gSIP(x,y,p) = y[1] + cos(x[1]-p[1]/90) - p[1]
+xBnds = [Interval(0.5,8.0)]
+yBnds = [Interval(68.8,149.9)]
+pBnds = [Interval(80,120)]
+impout1 = Implicit_SIP_Solve(f,h,hj,gSIP,xBnds,yBnds,pBnds,SIPopt1)
+
+# get solution values
+UBD = impout1.UBD               # upper bound
+LBD = impout1.LBD               # lower bound
+feas = impout1.feas             # is problem feasible?
+
+@test isapprox(LBD,-7.898552614446456,atol=1E-3)
+@test isapprox(UBD,-7.896382235986764,atol=1E-3)
+@test feas == true
+end
+
 end