Add powershell5 profile

config/win10/terminal/Powershell5_profile.ps1

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+Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+$msg = @"
+  _____   ______          ________ _____   _____ _    _ ______ _      _      _____
+ |  __ \ / __ \ \        / /  ____|  __ \ / ____| |  | |  ____| |    | |    | ____|
+ | |__) | |  | \ \  /\  / /| |__  | |__) | (___ | |__| | |__  | |    | |    | |__
+ |  ___/| |  | |\ \/  \/ / |  __| |  _  / \___ \|  __  |  __| | |    | |    |___ \
+ | |    | |__| | \  /\  /  | |____| | \ \ ____) | |  | | |____| |____| |____ ___) |
+ |_|     \____/   \/  \/   |______|_|  \_\_____/|_|  |_|______|______|______|____/
+"@
+
+function Hello {
+  param (
+    [string] $Msg
+  )
+
+  Write-Host $Msg -ForegroundColor DarkBlue
+}
+$(Hello $msg)
+$OutputEncoding = [console]::InputEncoding = [console]::OutputEncoding =
+                    New-Object System.Text.UTF8Encoding

snippet/python/rome_test.py

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+# coding=utf-8
+
+from rsome import dro
+# from rsome import grb_solver as grb
+from rsome import eco_solver as eco
+import rsome as rso
+import numpy as np
+
+
+def simple_linear():
+    model = dro.Model('LP model')        # create a Model object
+    x = model.dvar()                    # define a decision variable x
+    y = model.dvar()                    # define a decision variable y
+
+    model.max(3*x + 4*y)                # maximize the objective function
+    model.st(2.5*x + y <= 20)           # specify the 1st constraints
+    model.st(5*x + 3*y <= 30)           # specify the 2nd constraints
+    model.st(x + 2*y <= 16)             # specify the 3rd constraints
+    model.st(abs(y) <= 2)               # specify the 4th constraints
+
+    model.solve()                       # solve the model by the default solver
+
+
+def main():
+    model = dro.Model("electricSystem")
+
+    # w = model.dvar(w)
+    model.solve(eco)
+
+
+if __name__ == "__main__":
+    # simple_linear()
+    main()

snippet/python/rsome_example.py

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+# coding=utf-8
+
+from rsome import ro
+from rsome import eco_solver as grb
+import rsome as rso
+import numpy as np
+
+
+def main():
+    n = 150                                 # number of stocks
+    i = np.arange(1, n+1)                   # indices of stocks
+    p = 1.15 + i*0.05/150                   # mean returns
+    delta = 0.05/450 * (2*i*n*(n+1))**0.5   # deviations of returns
+    Gamma = 5                               # budget of uncertainty
+
+    model = ro.Model()
+    x = model.dvar(n)                       # fractions of investment
+    z = model.rvar(n)                       # random variables
+
+    model.maxmin((p + delta*z) @ x,         # the max-min objective
+                 rso.norm(z, np.infty) <= 1,  # uncertainty set constraints
+                 rso.norm(z, 1) <= Gamma)   # uncertainty set constraints
+    model.st(sum(x) == 1)                   # summation of x is one
+    model.st(x >= 0)                        # x is non-negative
+    model.solve(grb)                        # solve the model by Gurobi
+
+
+if __name__ == "__main__":
+    main()