# locJ are the column pointers, and J contains the matrix values.
#
# indJ = np.array([0,2,0,1,2,0,3,1,3],int)
# locJ = np.array([0,2,5,7,9],int)
# J = np.array([100.0, 2.0, 100.0, 100.0, 4.0, 1.0, 3.0, 1.0, 5.0],float)
bl = -inf*np.ones(n+m)
bu = inf*np.ones(n+m)
bl[2] = 0.0
bl[3] = 0.0
bl[4] = 2.0
bu[4] = 2.0
bl[5] = 4.0
bu[5] = 4.0
bl[6] = 0.0
iObj = 4
snoptb.setOption('Verbose',True)
names = np.array(['12345678']*(n+m))
snoptb.snoptb(name=' sntoyb',m=m,n=n,nnCon=nnCon,nnObj=nnObj,nnJac=nnJac,iObj=iObj,\
bl=bl,bu=bu,J=J,funcon=toycon,funobj=toyobj,Names=names)
valJ[46] = 0.0
# Column 9.
# Nonlinear elements in rows [5, 9, 11, 13, 14].
locJ[8] = 47
indJ[47] = 4
indJ[48] = 8
indJ[49] = 10
indJ[50] = 12
indJ[51] = 13
valJ[47] = 0.0
valJ[48] = 0.0
valJ[49] = 0.0
valJ[50] = 0.0
valJ[51] = 0.0
# Don't forget to finish off locJ.
# This is crucial.
locJ[ 9] = 51 + 1
Names = np.array(['12345678']*n)
snoptb.setOption('Verbose',True)
result = snoptb.snoptb(name=' snmainb',m=m,n=n,ne=ne,nnCon=nnCon,nnObj=nnObj,nnJac=nnJac,\
x0=x,bl=bl,bu=bu,J=valJ,indJ=indJ,locJ=locJ,funcon=hexCon,funobj=hexObj,Names=Names)
# Alternatively, the user can provide the sparsity pattern in
# sparse-by-column format. Here, indJ contains the row indices,
# locJ are the column pointers, and J contains the matrix values.
#
# indJ = np.array([0,2,0,1,2,0,3,1,3],int)
# locJ = np.array([0,2,5,7,9],int)
# J = np.array([100.0, 2.0, 100.0, 100.0, 4.0, 1.0, 3.0, 1.0, 5.0],float)
bl = -inf * np.ones(n + m)
bu = inf * np.ones(n + m)
bl[2] = 0.0
bl[3] = 0.0
bl[4] = 2.0
bu[4] = 2.0
bl[5] = 4.0
bu[5] = 4.0
bl[6] = 0.0
iObj = 4
snoptb.setOption('Verbose', True)
names = np.array(['12345678'] * (n + m))
snoptb.snoptb(name=' sntoyb',m=m,n=n,nnCon=nnCon,nnObj=nnObj,nnJac=nnJac,iObj=iObj,\
bl=bl,bu=bu,J=J,funcon=toycon,funobj=toyobj,Names=names)
valJ[45] = 0.0
valJ[46] = 0.0
# Column 9.
# Nonlinear elements in rows [5, 9, 11, 13, 14].
locJ[8] = 47
indJ[47] = 4
indJ[48] = 8
indJ[49] = 10
indJ[50] = 12
indJ[51] = 13
valJ[47] = 0.0
valJ[48] = 0.0
valJ[49] = 0.0
valJ[50] = 0.0
valJ[51] = 0.0
# Don't forget to finish off locJ.
# This is crucial.
locJ[9] = 51 + 1
Names = np.array(['12345678'] * n)
snoptb.setOption('Verbose', True)
result = snoptb.snoptb(name=' snmainb',m=m,n=n,ne=ne,nnCon=nnCon,nnObj=nnObj,nnJac=nnJac,\
x0=x,bl=bl,bu=bu,J=valJ,indJ=indJ,locJ=locJ,funcon=hexCon,funobj=hexObj,Names=Names)
