def buildandsolve(y): # y[i+1]-y[i]>0
with Model("Max likelihood") as M:
#M.setLogHandler(sys.stdout) # Make sure we get some output
n = len(y)
t = M.variable('t', 1, Domain.unbounded())
x = M.variable('x', n, Domain.greaterThan(0.0))
dy = [y[i+1]-y[i] for i in range(0,n-1)]
eleft = Expr.mulElm(dy[1:n-1],x.slice(0,n-2))
emid = Expr.add(Expr.mulElm(dy[0:n-2],x.slice(1,n-1)),Expr.mulElm(dy[1:n-1],x.slice(1,n-1)))
eright = Expr.mulElm(dy[0:n-2],x.slice(2,n))
# Debug print: print(eleft.toString())
M.constraint('convex',Expr.sub(Expr.sub(emid,eleft),eright),Domain.equalsTo(0.0))
M.constraint('area',Expr.mul(0.5,Expr.dot(dy,Expr.add(x.slice(0,n-1),x.slice(1,n)))),Domain.equalsTo(1.0))
gmmeancone.appendcone(M,t,x)
M.objective(ObjectiveSense.Maximize, t)
M.solve()
return x.level()
import sys
import mosek
from mosek.fusion import *
import gmmeancone
v = 100.0
for n in range(2,11):
with Model("Testing") as M:
t = M.variable('t', 1, Domain.unbounded())
x = M.variable('x', n, Domain.unbounded())
#(x[0]*...*x[n-1]) >= t^n, x,t>=0
gmmeancone.appendcone(M,t,x)
# x[0] <= v
M.constraint('xltv',x.index(0),Domain.lessThan(v))
# x[i]=1.0, for i=1,...,n-1
c = M.constraint('fixtoone',x.slice(1,n),Domain.equalsTo(1.0))
print(c.toString())
M.objective(ObjectiveSense.Maximize, t)
M.writeTask('dump.opf')
M.solve()
import sys
import mosek
from mosek.fusion import *
import gmmeancone
v = 100.0
for n in range(2, 11):
with Model("Testing") as M:
t = M.variable('t', 1, Domain.unbounded())
x = M.variable('x', n, Domain.unbounded())
#(x[0]*...*x[n-1]) >= t^n, x,t>=0
gmmeancone.appendcone(M, t, x)
# x[0] <= v
M.constraint('xltv', x.index(0), Domain.lessThan(v))
# x[i]=1.0, for i=1,...,n-1
c = M.constraint('fixtoone', x.slice(1, n), Domain.equalsTo(1.0))
print(c.toString())
M.objective(ObjectiveSense.Maximize, t)
M.writeTask('dump.opf')
M.solve()
