CMLMT uses the Sequential Quadratic Programming method. In this method, the parameters are updated in a series of iterations beginning with starting values that you provide. Let \(\theta_k\) be the current parameter values. Then the succeeding values are

\[\theta_{t+1} = \theta_k + \rho \delta,\]

where \(\delta\) is a \(K \times 1\) direction vector and \(\rho\) is a scalar step length.



\[\Sigma(\theta) = \frac{\partial^2L}{\partial\theta \partial \theta \prime}\]
\[\Psi(\theta) = \frac{\partial L}{\partial\theta}\]

and the Jacobians

\[\dot{G}(\theta) = \frac{\partial G(\theta)}{\partial\theta}\]
\[\dot{H}(\theta) = \frac{\partial H(\theta)}{\partial\theta}\]

For the purposes of this exposition and without loss of generality, we may assume that the linear constraints and bounds have been incorporated into \(G\) and \(H\).

The direction \(\delta\) is the solution to the quadratic program.

\[\text{minimize } \frac{1}{2}\delta\prime\Sigma(\theta_t)\delta + \Psi(\theta_t)\delta\]
\[\text{subject to } \dot{G}(\theta_t)\delta + G(\theta_t) = 0\]
\[\dot{H}(\theta_t)\delta + H(\theta_t) \geq 0\]

This solution requires that \(\Sigma\) be positive semi-definite.

In practice, linear constraints are specified separately from the \(G\) and \(H\) because their Jacobians are known and easy to compute. And the bounds are more easily handled separately from the linear inequality constraints.

Trust Radius

By default, a “trust radius” is set around all of the parameters being estimated. Constraints are set for each parameter that bounds the new direction, ensuring the iterations against extreme movements in the estimates. This provides for safer iterations but can add to the total number of iterations to convergence. To turn this off, set the trustRadius member of the instance of the cmlmtControl structure to a scalar missing value.

The Secant Algorithms

The Hessian may be very expensive to compute at every iteration, and poor start values may produce an ill-conditioned Hessian. For these reasons, alternative algorithms are provided in CMLMT for updating the Hessian rather than computing it directly at each iteration. These algorithms, as well as step length methods, may be modified during the execution of CMLMT.

Beginning with an initial estimate of the Hessian or a conformable identity matrix, an update is calculated. The update at each iteration adds more “information” to the estimate of the Hessian, improving its ability to project the direction of the descent. Thus, after several iterations, the secant algorithm should do nearly as well as the Newton iteration with much less computation.

There are two basic types of secant methods: the BFGS (Broyden, Fletcher, Goldfarb, and Shanno) and the DFP (Davidon, Fletcher, and Powell). They are both rank two updates, that is, they are analogous to adding two rows of new data to a previously computed moment matrix. The Cholesky factorization of the estimate of the Hessian is updated using the functions cholup() and choldn().

Secant Methods (BFGS and DFP)

BFGS is the method of Broyden, Fletcher, Goldfarb, and Shanno, and DFP is the method of Davidon, Fletcher, and Powell. These methods are complementary (Luenberger 1984, page 268). BFGS and DFP are like the NEWTON method in that they use both first and second derivative information. However, in DFP and BFGS the Hessian is approximated, reducing considerably the computational requirements. Because they do not explicitly calculate the second derivatives they are sometimes called quasi-Newton methods. While it takes more iterations than the NEWTON method, the use of an approximation produces a gain because it can be expected to converge in less overall time (unless analytical second derivatives are available in which case it might be a toss-up).

The secant methods are commonly implemented as updates of the inverse of the Hessian. This is not the best method numerically for the BFGS algorithm (Gill and Murray, 1972). This version of CMLMT, following Gill and Murray (1972), updates the Cholesky factorization of the Hessian instead, using the functions cholup() and choldn() for BFGS. The new direction is then computed using cholsol(), a Cholesky solve, as applied to the updated Cholesky factorization of the Hessian and the gradient.

Line Search Methods

Given a direction vector delta, the updated estimate of the parameters is computed

\[\theta_{t+1} = \theta_t + \rho\delta,\]

where rho is a constant, usually called the step length, that increases the descent of the function given the direction. CMLMT includes a variety of methods for computing \(\rho\). The value of the function to be minimized as a function of \(\rho\) is

\[m(\theta_t + \rho\delta)\]

Given \(\theta\) and \(\delta\), this is a function of a single variable \(\rho\). Line search methods attempt to find a value for \(\rho\) that decreases \(m\). STEPBT is a polynomial fitting method, BRENT and HALF are iterative search methods. A fourth method called ONE forces a step length of 1. The default line search method is STEPBT. If this or any selected method fails, then BRENT is tried. If BRENT fails, then HALF is tried. If all of the line search methods fail, then a random search is tried, provided the randRadius member of the cmlmtControl instance is greater than zero. The default setting for randRadius is greater than zero.

Augmented Penalty Line Search Method

When the lineSearch member of the instance of the cmlmtControl structure is set to zero, CMLMT uses an “augmented Lagrangian penalty”’ method for the line search described in Conn, Gould, and Toint (2000). The Hessian and gradient for the Quadratic Programming problem in the SQP method is augmented as described in their Section 15.3.1. This method requires that constraints be imposed on the parameters. This method is not available for solving maximum likelihood problems without constraints on parameters.


STEPBT is an implementation of a similarly named algorithm described in Dennis and Schnabel (1983). It first attempts to fit a quadratic function to \(m(\theta_t + \rho\delta)\) and computes a \(rho\) that minimizes the quadratic. If that fails, it attempts to fit a cubic function. The cubic function more accurately portrays the \(F\) which is not likely to be very quadratic but is, however, more costly to compute. STEPBT is the default line search method because it generally produces the best results for the least cost in computational resources.


This method is a variation on the golden section method due to Brent (1972). In this method, the function is evaluated at a sequence of test values for \(rho\). These test values are determined by extrapolation and interpolation using the constant:

\[(\sqrt{5 - 1})/2 = 0.6180 \ldots\]

This constant is the inverse of the so-called “golden ratio”:

\[(\sqrt{5 + 1})/2 = 1.6180 \ldots\]

and is why the method is called a golden section method. This method is generally more efficient than STEPBT but requires significantly more function evaluations.


This method first computes \(m(x + \delta)\), i.e., sets \(\rho = 1\). If \(m(x + \delta) \leq m(x)\) then the step length is set to 1. If not, then it tries \(m(x + 0.5\delta)\). The attempted step length is divided by one half each time the function fails to decrease and exits with the current value when it does decrease. This method usually requires the fewest function evaluations (it often only requires one), but it is the least efficient in that it is not very likely to find the step length that decreases \(m\) the most.


The Strong Wolfe condition requires

\[m(\theta_t + \delta) \leq m(\theta_t) + g_1\rho \theta_t' [ m(\theta_t) - m(\theta_{t-1})]\]
\[\theta_t [ m(\theta_t + \rho) - m(\theta_{t-1} + \rho)] + \delta) \leq + g_2\rho \theta_t' [ m(\theta_t) - m(\theta_{t-1})]\]


\[0 < g_1 < g_2< 1\]

The first condition ensures \(m\) that decreases sufficiently and the second condition ensures that the slope has been reduced sufficiently.


This is a variation on the golden search method. A sequence of step lengths are computed, interpolating or extrapolating using a golden ratio, and the method exits when the function decreases.

Weighted Maximum Likelihood

Weights are specified by setting the weights member of the cmlmtControl instance to a weighting vector or by assigning it the name of a column in the GAUSS data set being used in the estimation.

CMLMT assumes that the weights sum to the number of observations, i.e, that the weights are frequencies. This will be an issue only with statistical inference. Otherwise, any multiple of the weights will produce the same results.

Active and Inactive Parameters

The member active of the cmlmtControl structure may be used to fix parameters to their start values. active must be set to a vector of the same length as the vector of start values. Elements of active set to zero will fix the corresponding parameter to its starting value, while the parameters corresponding to nonzero elements will be estimated.

For example, to fix the third parameter of a model with five parameters, to its starting value:

// Declare 'ctl' to be a cmlmtControl struct
struct cmlmtControl ctl;

// Fill 'ctl' with default settings
ctl = cmlmtControlCreate();

// Fix the third parameter to its start value
// NOTE: This setting will not take effect until
// 'ctl' is passed in to a call to 'cmlmt' = { 1, 1, 0, 1, 1 };

This allows estimation of different models without having to modify the function procedure. For example, setting a parameter to be ‘inactive’ and setting its starting value to zero will eliminate it from the estimation.