@@ -897,7 +897,8 @@ def __init__(
897897def solve_ocp (
898898 sys , horizon , X0 , cost , trajectory_constraints = None , terminal_cost = None ,
899899 terminal_constraints = [], initial_guess = None , basis = None , squeeze = None ,
900- transpose = None , return_states = True , log = False , ** kwargs ):
900+ transpose = None , return_states = True , print_summary = True , log = False ,
901+ ** kwargs ):
901902
902903 """Compute the solution to an optimal control problem
903904
@@ -951,6 +952,9 @@ def solve_ocp(
951952 log : bool, optional
952953 If `True`, turn on logging messages (using Python logging module).
953954
955+ print_summary : bool, optional
956+ If `True` (default), print a short summary of the computation.
957+
954958 return_states : bool, optional
955959 If True, return the values of the state at each time (default = True).
956960
@@ -1017,7 +1021,8 @@ def solve_ocp(
10171021
10181022 # Solve for the optimal input from the current state
10191023 return ocp .compute_trajectory (
1020- X0 , squeeze = squeeze , transpose = transpose , return_states = return_states )
1024+ X0 , squeeze = squeeze , transpose = transpose , print_summary = print_summary ,
1025+ return_states = return_states )
10211026
10221027
10231028# Create a model predictive controller for an optimal control problem
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