Ytukyg

I am new to doing optimization with Python and Gurobi. I have tried to code the constraint

f_{s}^{E}=x_{i}sum_{1}^{i-1}ALW_{js},forall sin S,forall i={1,2,...,frac{c(c-1)}{2}}

Where A_{s}^{E} and x_{i} are variables.
To calculate ALW_{js} we need to read the upper triangle of a distance matrix and then sort the distances, d_{ij} for i,jinC and j>i, descending. The sorted result can be represented as follows:

d_{1} geqslant d_{2} geqslant ... geqslant d_{frac{c(c-1)}{2}}

Where d_{1}=max⁡{d_{ij}} and d_{frac{c(c-1)}{2}=min{d_{ij}}.
Each sorted distance above, meaning d_{1}, d_{2}, ..., d_{frac{c(c-1)}{2}}, has a corresponding value in scenario s

ALN_{ijs}=(1-0.05)(1-0.72) max left { AN_{is},AN{js} right }, forall sin S,forall i,jin C and j>i

Where AN_{is} is read from a file. ALN_{ijs} can be written

ALW_{js}, forall sin S,forall i={1,2,...,frac{c(c-1)}{2}}

My codes for this are below. The Gurobi provides the solution, but it says the constraint

f_{s}^{E}=x_{i}sum_{1}^{i-1}ALW_{js},forall sin S,forall i={1,2,...,frac{c(c-1)}{2}}

Is quadratic, which is not really. I would appreciate if anybody can guide me.

x={}

fE={}

for s in range (S):

    fE[s]=Z.addVar(lb=0, vtype=GRB.CONTINUOUS, name='fE%s'%(s))



DtN={}

with open('Distances.csv', 'rU') as file:

    table = [row for row in csv.reader(file)]

    for i in range (C):

        for j in range (C):

            if j>i:

                DtN[i,j]=round(-1*float(table[i][j]),2)

SortDis=

SortDisKey=

for key, value in sorted(DtN.iteritems(), key=lambda (k,v): (v,k)):

    SortDis.append(abs(value))

    SortDisKey.append(key) 

for i in range (len(DtN)):

    x[i]=Z.addVar(lb=0, vtype=GRB.BINARY, name='x%s'%(i))



with open('Feed1.txt', 'r') as Fee:

    for i in range(C):

        Feed= round(float(Fee.readline()),3)

        for s in L11:

            AN[i,s]=round(Feed/10**6,9)

        for s in L12:

            AN[i,s] = round(Feed*1.28/10**6,9)

        for s in L13:

            AN[i,s] = round(Feed*0.95/10**6,9)

ALN={}

for s in range (S):

    ALN[s]={}



for s in range(S):

    for i in range(C):

        for j in range(C):

            if j>i:

                ALN[s][i,j]=max((1-0.05)*(1-0.72)*AN[i,s],(1-0.05)*(1-0.72)*AN[j,s])

ALW={}

for s in range (S):

    ALW[s]=



for s in range (S):

    for j in SortDisKey:    

        ALW[s].append(ALN[s][j])

for s in range (S):

    for i in range (len(DtN)):

        Z.addConstr(fE[s]>=(x[i]*(quicksum(ALW[s][j] for j in range (0,i-1)))), name='N3%s%s'%(s,i))