Correct way to define constraints in Pyomo

Question

Can I know if the constraint below can be defined as follows in Pyomo for convex optimization \begin{align}\forall k,\quad W_{ik}+G_{ik}\begin{cases}\in[0,m_i\Delta t],&\quad\forall i:t+k\Delta t\le d_i\\=0,&\quad\forall i:t+k\Delta t>d_i\end{cases}\end{align} where $W$ and $G$ are arrays of dimension $M\times N$?

del_t = 5
M = 2 # set of active tasks
N = 4 # 4 time steps
maxP = 0.14
d = np.array([20,20]) # deadline in seconds
curr_time = 0 


## Third constraint START ##
m.c3 = []
for i in range(M):
    for k in range(N):
        if curr_time + k*del_t <= d[i] + curr_time:
            c3_exp = m.W[i+1,k+1] + m.G[i+1,k+1] <= maxP*del_t
            m.c3.append(Constraint(expr= c3_exp))
            print(m.c3[i])
        else:
            c3_exp = m.W[i+1,k+1] + m.G[i+1,k+1] == 0
            m.c3.append(Constraint(expr= c3_exp))
            print(m.c3[i])
## Third constraint END ##

Can I also know if the output I get below when I run this code is correct?

Answer 1

You should make use of sets. One way of doing it would be:

model = ConcreteModel()

# set
model.MNset = Set(initialize = [j for j in range(M*N))]) # j = M*(i-1) + k

# variables
model.W = Var(model.MNset, domain=Reals)
model.G = Var(model.MNset, domain=Reals)

# constraints
def WG_constraint_upper(m,j):
   i = floor(j/M)+1
   if tt + j*delta_t <= dd[i]:
      return m.W[j] + m.G[j] <= 0
   else :
      return m.W[j] + m.G[j] <= mm[i]*delta_t

def WG_constraint_lower(m,j):
  return m.W[j] + m.G[j] >= 0

model.WG_constraint_upper = Constraint(model.MNset,rule = WG_constraint_upper)
model.WG_constraint_lower = Constraint(model.MNset,rule = WG_constraint_lower )

Another way would be to define a two dimensional MNset so you would directly access i and k.

I also suggest you take a look at Pyomo Fundamentals