Maximize . Along the positive- trajectory use Hamiltonian of an optimal-control problem . The terminal costate is , and
Thus . Maximizing over gives as long as . The state equation integrates to , and the adjoint equation gives , validating the maximization. Hence
This is globally optimal, not just an extremal: whenever , . If a competitor reaches and later becomes positive, apply that bound after its last zero; it has even less remaining time to attain a positive terminal height. A negative terminal height cannot improve this positive maximum. Therefore the minimum objective is .

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