dense.py
#！/usr/bin/env python3.7 # 2023年版权,GurobiOptimization, LLC # This example formulates and solves the following simple QP model: # # minimize x + y + x^2 + x*y + y^2 + y*z + z^2 # subject to x + 2 y + 3 z >= 4 # x + y >= 1 # x, y, z non-negative # # The example illustrates the use of dense matrices to store A and Q # (and dense vectors for the other relevant data). We don't recommend # that you use dense matrices, but this example may be helpful if you # already have your data in this format. import sys import gurobipy as gp from gurobipy import GRB def dense_optimize(rows, cols, c, Q, A, sense, rhs, lb, ub, vtype, solution): model = gp.Model() # Add variables to model vars = [] for j in range(cols): vars.append(model.addVar(lb=lb[j], ub=ub[j], vtype=vtype[j])) # Populate A matrix for i in range(rows): expr = gp.LinExpr() for j in range(cols): if A[i][j] != 0: expr += A[i][j]*vars[j] model.addLConstr(expr, sense[i], rhs[i]) # Populate objective obj = gp.QuadExpr() for i in range(cols): for j in range(cols): if Q[i][j] != 0: obj += Q[i][j]*vars[i]*vars[j] for j in range(cols): if c[j] != 0: obj += c[j]*vars[j] model.setObjective(obj) # Solve model.optimize() # Write model to a file model.write('dense.lp') if model.status == GRB.OPTIMAL: x = model.getAttr('X', vars) for i in range(cols): solution[i] = x[i] return True else: return False # Put model data into dense matrices c = [1, 1, 0] Q = [[1, 1, 0], [0, 1, 1], [0, 0, 1]] A = [[1, 2, 3], [1, 1, 0]] sense = [GRB.GREATER_EQUAL, GRB.GREATER_EQUAL] rhs = [4, 1] lb = [0, 0, 0] ub = [GRB.INFINITY, GRB.INFINITY, GRB.INFINITY] vtype = [GRB.CONTINUOUS, GRB.CONTINUOUS, GRB.CONTINUOUS] sol = [0]*3 # Optimize success = dense_optimize(2, 3, c, Q, A, sense, rhs, lb, ub, vtype, sol) if success: print('x: %g, y: %g, z: %g' % (sol[0], sol[1], sol[2]))
