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Tienstra's method was developed to solve parameter adjustment with linear equality constraints, which has been otherwise often carried out by directly applying the conventional (or standard) method of Lagrange multipliers to quadratic optimization problems with a positive definite matrix. We analyze the computational complexity of the celebrated Tienstra's method and compare it with that of the method of Lagrange multipliers. We show that Tienstra's method is not only statistically elegant but also interestingly of significant computational advantage over the method of Lagrange multipliers to solve weighted least squares adjustment with linear equality constraints, with the saving of computational cost by a minimum of about 38% to a maximum of 100%. Tienstra's method can be very important for large scale problems of adjustment and inversion with linear equality constraints. [ABSTRACT FROM AUTHOR]