Lotka Volterra fishing problem (Casadi)
From mintOC
This page contains a discretized version of the MIOCP Lotka Volterra fishing problem in Casadi format.
Casadi
The model in Python code for a fixed control discretization grid using direct multiple shooting.
# ------------------------------------------------------------------------------ # Lotka Volterra fishing problem with CasADi Python interface using direct # multiple shooting with IPOPT and Sundials' integrator CVodes # # NOTE: CasADi currently has no support for mixed-integer problems. Only the # relaxed solution is calculated # ------------------------------------------------------------------------------ # Import from casadi import * # Introduce model parameters t0 = 0 tf = 12 c0 = 0.4 c1 = 0.2 x0 = [0.5, 0.7, 0] # Define length of time horizon as well as number and length of intervals T = tf - t0 N = 100 dt = float(T) / N # Define the DAE x = SX.sym('x', 3) w = SX.sym('w') rhs = vertcat([ dt * ( x[0] - x[0] * x[1] - c0 * x[0] * w), dt * (-x[1] + x[0] * x[1] - c1 * x[1] * w), dt * ((x[0] - 1)**2 + (x[1] - 1)**2) ]) dae = SXFunction("dae", daeIn(x=x, p=w), daeOut(ode=rhs)) I_opt = { 'linear_solver': 'csparse', 't0': 0.0, 'tf': 1.0 } I = Integrator("integrate", "cvodes", dae, I_opt) # Define number of state variables, controls and variables per interval nx = x.size1() # Number of states nu = w.size1() # Number of controls ns = nx + nu # Number of variables per shooting interval # Create symbolic expressions for actual NLP variables and define DMS structure X = MX.sym('X', (N + 1) * nx + N * nu) # Optimization variables G = [] # Nonlinear constraints F = X[N*ns+2] # Mayer objective for i in range(0, N): X0 = X[i*ns:i*ns+nx] # Initial state for interval U = X[i*ns+nx:(i+1)*ns] # Control for interval X1 = X[(i+1)*ns:(i+1)*ns+nx] # Terminal state for interval Y = I({'x0': X0, 'p': U}) G = vertcat([G, Y['xf'] - X1]) # Define NLP nlp = MXFunction('nlp', nlpIn(x=X), nlpOut(f=F, g=G)) # Create feasible initial solution by simulating with constant zero control Xstart = DMatrix.zeros(X.size1(), X.size2()) Xstart[0:nx] = x0 for i in range(0, N): X0 = Xstart[i*ns:i*ns+nx] Xstart[i*ns+nx:(i+1)*ns] = 0.0 Xstart[(i+1)*ns:(i+1)*ns+nx] = I({'x0': X0, 'p': 0.0})['xf'] # Create and execute nonlinear solver solver = NlpSolver('solver', 'ipopt', nlp) result = solver({ 'x0': Xstart, 'lbg': DMatrix.zeros(G.size1()), 'ubg': DMatrix.zeros(G.size1()), 'lbx': x0 + [0.0] + [0.0, 0.0, 0.0, 0.0] * (N - 1) + [0.0, 0.0, 0.0], 'ubx': x0 + [1.0] + [inf, inf, inf, 1.0] * (N - 1) + [inf, inf, inf] }) # Extract solution and objective x = result['x'] f = result['f'] # Disassemble solution into easily plottable lists t = [i*dt for i in range(0, N + 1)] x0 = [x[i*ns] for i in range(0, N + 1)] x1 = [x[i*ns+1] for i in range(0, N + 1)] u = [x[i*ns+nx] for i in range(0, N )]
Results
IPOPT
The relaxed solution calculated by IPOPT (Ipopt 3.12, Linux x86_64, default settings, 4 GHz quadcore, Linux 4.2.0-23-generic) has an objective function value of 
. IPOPT requires 22 iterations (6.062 seconds of processing time). The following is a Gnuplot compatible tabular version of the solution:
# t, x_0, x_1, u
0, 0.5, 0.7, 5.18436e-09
0.12, 0.519594, 0.659989, 5.42459e-09
0.24, 0.542429, 0.623852, 5.79496e-09
0.36, 0.568601, 0.591429, 6.30563e-09
0.48, 0.59823, 0.562571, 6.97415e-09
0.6, 0.631456, 0.537143, 7.827e-09
0.72, 0.66843, 0.515028, 8.90236e-09
0.84, 0.709312, 0.496134, 1.02545e-08
0.96, 0.75426, 0.480401, 1.19611e-08
1.08, 0.803421, 0.4678, 1.41346e-08
1.2, 0.856919, 0.458344, 1.69417e-08
1.32, 0.914842, 0.452091, 2.06377e-08
1.44, 0.97722, 0.449154, 2.56285e-08
1.56, 1.044, 0.449708, 3.2592e-08
1.68, 1.11503, 0.454003, 4.27322e-08
1.8, 1.18998, 0.462372, 5.83658e-08
1.92, 1.26837, 0.475251, 8.44574e-08
2.04, 1.34942, 0.493189, 1.33425e-07
2.16, 1.43209, 0.516862, 2.44977e-07
2.28, 1.51492, 0.547087, 6.24464e-07
2.4, 1.59605, 0.584816, 0.715281
2.52, 1.61776, 0.61833, 0.999999
2.64, 1.61116, 0.6499, 0.999999
2.76, 1.59844, 0.68229, 1
2.88, 1.57963, 0.714939, 1
3, 1.55497, 0.747196, 1
3.12, 1.52487, 0.778343, 0.999999
3.24, 1.48993, 0.807625, 0.999999
3.36, 1.4509, 0.83429, 0.999999
3.48, 1.40865, 0.857635, 0.999998
3.6, 1.36412, 0.877047, 0.999997
3.72, 1.31827, 0.892039, 0.999991
3.84, 1.27203, 0.90228, 0.999646
3.96, 1.22628, 0.907616, 0.536112
4.08, 1.20755, 0.919653, 0.581667
4.2, 1.18502, 0.928525, 0.503883
4.32, 1.16606, 0.936899, 0.464153
4.44, 1.14851, 0.944178, 0.418428
4.56, 1.13278, 0.950658, 0.378617
4.68, 1.11859, 0.956378, 0.341493
4.8, 1.10582, 0.961433, 0.3073
4.92, 1.09438, 0.965904, 0.276462
5.04, 1.08411, 0.969849, 0.248219
5.16, 1.07492, 0.973333, 0.222481
5.28, 1.0667, 0.976412, 0.19935
5.4, 1.05936, 0.97913, 0.178262
5.52, 1.0528, 0.981533, 0.159402
5.64, 1.04696, 0.983656, 0.142349
5.76, 1.04175, 0.985532, 0.127041
5.88, 1.0371, 0.987191, 0.1133
6, 1.03297, 0.988657, 0.100985
6.12, 1.02929, 0.989954, 0.0899604
6.24, 1.02601, 0.991101, 0.0800981
6.36, 1.0231, 0.992117, 0.0712934
6.48, 1.02051, 0.993015, 0.0634308
6.6, 1.01821, 0.993811, 0.0564127
6.72, 1.01617, 0.994515, 0.0501573
6.84, 1.01435, 0.995139, 0.0445824
6.96, 1.01274, 0.995691, 0.0396177
7.08, 1.01131, 0.99618, 0.0351979
7.2, 1.01003, 0.996613, 0.0312714
7.32, 1.0089, 0.996997, 0.027765
7.44, 1.0079, 0.997337, 0.0246626
7.56, 1.00701, 0.997639, 0.0218975
7.68, 1.00622, 0.997905, 0.0194423
7.8, 1.00552, 0.998142, 0.0172603
7.92, 1.0049, 0.998351, 0.0153224
8.04, 1.00435, 0.998537, 0.0136015
8.16, 1.00386, 0.998701, 0.0120735
8.28, 1.00342, 0.998847, 0.0107171
8.4, 1.00303, 0.998976, 0.00951328
8.52, 1.00269, 0.99909, 0.00844491
8.64, 1.00239, 0.999191, 0.00749702
8.76, 1.00212, 0.999281, 0.00665598
8.88, 1.00188, 0.99936, 0.00590975
9, 1.00167, 0.99943, 0.00524776
9.12, 1.00148, 0.999492, 0.00466052
9.24, 1.00131, 0.999547, 0.00413914
9.36, 1.00117, 0.999596, 0.00367206
9.48, 1.00103, 0.999637, 0.00326159
9.6, 1.00092, 0.999675, 0.00290302
9.72, 1.00082, 0.999707, 0.00258504
9.84, 1.00072, 0.999736, 0.00230262
9.96, 1.00064, 0.999762, 0.00205186
10.08, 1.00057, 0.999785, 0.00182951
10.2, 1.00051, 0.999805, 0.00163302
10.32, 1.00045, 0.999822, 0.00146039
10.44, 1.0004, 0.999838, 0.00131011
10.56, 1.00036, 0.999851, 0.00118097
10.68, 1.00032, 0.999863, 0.00107188
10.8, 1.00028, 0.999872, 0.000981659
10.92, 1.00025, 0.99988, 0.00090944
11.04, 1.00022, 0.999886, 0.000854893
11.16, 1.00019, 0.99989, 0.000818284
11.28, 1.00017, 0.999892, 0.000801181
11.4, 1.00014, 0.999891, 0.000807844
11.52, 1.00012, 0.999886, 0.000848734
11.64, 1.00009, 0.999878, 0.000951928
11.76, 1.00006, 0.999864, 0.00121852
11.88, 1.00002, 0.999838, 0.00267036
12, 0.999915, 0.999769, 0
