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+# # Rocket Launch
+# We will solve an optimal control problem that aims to minimize final time.
+
+# # Problem Statement and Model
+# In this problem, we seek to minimize the final time ``t_f`` of a rocket launch between 0 and 10 metres.
+# Minimizing fuel use is also important for this problem. The force- or thrust- of the rocket can be between
+# -1.1 and 1.1, while the velocity of the rocket cannot exceed 1.7. The drag resistance of the rocket is
+# proportional to the square of the velocity. The mass of the rocket decreases as fuel is burned over time. 
+# Our optimization guidelines are subject to these constraints:
+# ```math
+# \begin{aligned}
+#   &&\min t_f \\
+#   &&&\frac{ds}{dt}= v \\
+#   &&&\frac{dv}{dt}= (u-0.2*(v^2))/2\\
+#   &&&\frac{dm}{dt}= -0.01(u^2)\\
+#   &&&0 \leq v(t) \leq 1.7 \\
+#   &&&-1.1 \leq u(t) \leq 1.1 \\
+#       &&&s(0) = 0 \\
+#       &&&v(0) = 0 \\
+#       &&&m(0) = 1 \\
+#       &&&s(t_f) = 10 \\
+#       &&&v(t_f) = 0 \\
+# \end{aligned}
+# ```
+# where ``v(t)`` represents the rocket's velocity, ``s(t)`` represents the rocket's position and
+# ``m(t)`` represents the rocket's mass. Additionally, ``u(t)`` represents the rocket's force (or thrust).
+
+# # Modeling in InfiniteOpt
+# First we must import ``InfiniteOpt`` and other packages.
+using JuMP, InfiniteOpt, Ipopt, Plots;
+
+# # Model Initialization
+# We then initialize the infinite model with [`InfiniteModel`](@ref), selecting Ipopt as the desired optimizer to solve this model.
+model = InfiniteModel(Ipopt.Optimizer);
+
+# # Infinite Parameter Definition
+# We are defining the infinite parameter ``t \in [0, 1]``. The bounds give the optimizer parameters to iterate over.
+@infinite_parameter(model, t in [0,1], num_supports = 50, derivative_method = FiniteDifference(Backward()));
+
+# # Infinite Variable Definition
+# Moving on to specifying variables, we have v as velocity of the rocket, u as the force of the rocket.
+# The position, p, and mass, m, vary over time. The final time, t_f, must be between 0.1 and 100 seconds.
+@variable(model, 0 <= v <= 1.7, Infinite(t)) 
+@variable(model, -1.1 <= u <= 1.1, Infinite(t)) 
+@variable(model, 0.1 <= t_f <= 100) 
+@variable(model, p, Infinite(t))
+@variable(model, m, Infinite(t));
+
+# # Initial and Final Condition Definition
+# Now that the model is defined, we then want to specify both our initial and final conditions.
+@constraint(model, v(0) == 0)
+@constraint(model, v(1) == 0)
+@constraint(model, p(0) == 0)
+@constraint(model, p(1) == 10)
+@constraint(model, m(0) == 1)
+@constraint(model, m(1) >= 0.2);
+
+# # Objective Definition
+# We now add the objective, using `@objective`, which is to minimize final time t_f.
+@objective(model, Min, t_f);
+
+# # Constraint Definition
+# We finally are adding our constraints, which are defining the ODEs for our system model.
+@constraint(model, v * t_f == deriv(p, t))
+@constraint(model, ((u - (0.2 * (v ^ 2))) * t_f) == (deriv(v, t)) * m) 
+@constraint(model, (-0.01 * (u ^ 2)) * t_f == deriv(m, t));
+
+# # Problem Solution
+# Now that everything is set up, we can solve the model by using `@optimize!`
+optimize!(model)
+
+# # Extract and Plot the Results
+# We can now extract the optimized results and plot them to visualize four results graphically:
+# Position over time, velocity over time, mass over time and force over time. In addition, we can print the final time value of t_f.
+tf_data = value(t_f)
+t_data = supports(t) .* tf_data
+v_data = value(v)
+p_data = value(p)
+m_data = value(m)
+u_data = value(u);
+
+# Printing the final time value.
+println("Minimized Final Time: ", tf_data," seconds");
+
+# Creating the four plots as described above. 
+plot1 = plot(t_data, v_data, title="Velocity Over Time", linecolor = :red, xlabel="Time", label = "Velocity", ylabel="Velocity", lw=2);
+plot2 = plot(t_data, p_data, title="Position Over Time", linecolor = :orange, xlabel="Time", label = "Position", ylabel="Position", lw=2);
+plot3 = plot(t_data, m_data, title="Mass Over Time", linecolor = :yellow, xlabel="Time", label = "Position", ylabel="Position", lw=2);
+plot4 = plot(t_data, u_data, title="Force Over Time", linecolor = :limegreen, xlabel="Time", label = "Position", ylabel="Position", lw=2);
+
+# Visualizing all four plots on one display.
+display(plot(plot1, plot2, plot3, plot4, layout=(4,1), size = (1000, 1000)))
+
+# ### Maintenance Tests
+# These are here to ensure this example stays up to date. 
+using Test
+tol = 1E-4
+@test termination_status(model) == MOI.LOCALLY_SOLVED
+@test has_values(model)
+@test isapprox(objective_value(model), 7.4482495165303710, atol=tol)
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