warm start solver; make solver faster

Project.toml

@@ -1,7 +1,7 @@
 name = "RosSockets"
 uuid = "f2b1035b-1fed-4502-a057-be66ed18c291"
 authors = ["Jacob Levy"]
-version = "0.2.1"
+version = "0.2.2"
 
 [deps]
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"

examples/optimal_feedback_control_example.jl

@@ -22,6 +22,9 @@ function run_example()
     timestep = 0.1          # duration of each timestep (sec)
     model = initialize_model(goal_state, timestep)
 
+    # begin without warm start so that first solve is cold
+    warm_start = false
+
     # initialize a plotter to display trajectory solution real-time
     display(plot(xlims=(-1,4),ylims=(-1,4),size=(600,600)))
 
@@ -45,8 +48,11 @@ function run_example()
         feedback_state = receive_feedback_data(feedback_connection, timeout) 
 
         # compute control commands and send them
-        commands, trajectory = solve!(model,feedback_state)
+        commands, trajectory = solve!(model,feedback_state,warm_start)
         send_control_commands(robot_connection, commands)
+
+        # future calls to solve! will be warm started
+        warm_start = true
 
         # plot solution trajectory
         display(plot(trajectory[:,1],trajectory[:,2],

examples/utils/nlp.jl

@@ -17,6 +17,8 @@ function initialize_model(goal_state, timestep)
 
     model = Model(Ipopt.Optimizer)
     set_optimizer_attribute(model, "max_iter", 100)
+    set_optimizer_attribute(model, "print_level", 1)
+    set_optimizer_attribute(model, "mu_strategy", "adaptive")
 
     @variables(model, begin
         x[1:T]
@@ -62,10 +64,16 @@ function initialize_model(goal_state, timestep)
     return model
 end
 
-function solve!(model, feedback_state::FeedbackData)
+function solve!(model, feedback_state::FeedbackData, warm_start=false)
 
     state = to_state_vec(feedback_state)
 
+    if warm_start
+        x = all_variables(model)
+        x_solution = value.(x)
+        set_start_value.(x, x_solution)
+    end
+
     fix(model[:x][1], state[1])
     fix(model[:y][1], state[2])
     fix(model[:v][1], state[3])