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The NLP Procedure |
Algorithm | TECH= |
Linear Complementary Problem | LICOMP |
Quadratic Active Set Technique | QUADAS |
Trust-Region Method | TRUREG |
Newton-Raphson Method With Line Search | NEWRAP |
Newton-Raphson Method With Ridging | NRRIDG |
Quasi-Newton Methods (DBFGS, DDFP, BFGS, DFP) | QUANEW |
Double-Dogleg Method (DBFGS, DDFP) | DBLDOG |
Conjugate Gradient Methods (PB, FR, PR, CD) | CONGRA |
Nelder-Mead Simplex Method | NMSIMP |
Levenberg-Marquardt Method | LEVMAR |
Hybrid Quasi-Newton Methods (DBFGS, DDFP) | HYQUAN |
No algorithm for optimizing general nonlinear functions exists that will always find the global optimum for a general nonlinear minimization problem in a reasonable amount of time. Since no single optimization technique is invariably superior to others, PROC NLP provides a variety of optimization techniques that work well in various circumstances. However, it possible to devise problems for which none of the techniques in PROC NLP can find the correct solution. Moreover, nonlinear optimization can be computationally expensive in terms of time and memory so care must be taken when matching an algorithm to a problem.
All optimization techniques in PROC NLP use O(n^{2}) memory except the conjugate gradient methods, which uses only O(n) of memory and are designed to optimize problems with many variables. Since the techniques are iterative they require the repeated computation of:
However, since each of the optimizers requires different derivatives and supports different types of constraints some computational efficiencies can be gained. The following table shows, for each optimization technique, which derivatives are needed (FOD: first order derivatives; SOD: second order derivatives) and what kind of constraints (BC: boundary constraints; LIC: linear constraints, NLC: nonlinear constraints) are supported.
Algorithm | FOD | SOD | BC | LIC | NLC |
LICOMP | - | - | x | x | - |
QUADAS | - | - | x | x | - |
TRUREG | x | x | x | x | - |
NEWRAP | x | x | x | x | - |
NRRIDG | x | x | x | x | - |
QUANEW | x | - | x | x | x |
DBLDOG | x | - | x | x | - |
CONGRA | x | - | x | x | - |
NMSIMP | - | - | x | x | x |
LEVMAR | x | - | x | x | - |
HYQUAN | x | - | x | x | - |
The factors that go into choosing a particular optimizer for a particular problem are complex and may involve trial and error. The following should be taken into account: First, the structure of the problem has to be considered: Is it quadratic? least-squares? Does it have linear or nonlinear constraints? Next, it is important to consider the type of derivatives of the objective function and the constraints which are needed and whether these are analytically tractable or not. This section provides some guidelines for making the right choices.
For many optimization problems, computing the gradient takes more computer time than computing the function value, and computing the Hessian sometimes takes much more computer time and memory than computing the gradient, especially when there are many decision variables. Unfortunately, optimization techniques that do not use the Hessian usually require many more iterations than techniques that do use the (approximate) Hessian, and so are often slower. Techniques that do not use the Hessian also tend to be less reliable (e.g., terminating at local rather than global optima).
The (very general) function compiler does not compute derivatives very efficiently. This is particularly apparent for second-order derivatives. For large problems, memory and computer time can be saved by programming your own derivatives using the GRADIENT, JACOBIAN, CRPJAC, HESSIAN, and JACNLC statements. If the optimization problem depends on more than 50 parameters, and you are not able to specify first and second-order derivatives of the objective function, you are advised not to use an optimization technique that requires the computation of second derivatives.
The following provides some guidance for matching an algorithm to a particular problem.
The QUADAS and LICOMP algorithms can be used to minimize or maximize a quadratic objective function,
Simple boundary and general linear constraints can be specified using the BOUNDS or LINCON statement or an INQUAD= or INEST= data set.
The QUADAS algorithm is an active set method which iteratively updates the QT decomposition of the matrix A_{k} of active linear constraints and the Cholesky factor of the projected Hessian Z^{T}GZ simultaneously. The update of active boundary and linear constraints is done separately; see
Gill, Murray, Saunders, & Wright (1984). Here is Q an n_{free} ×n_{free} orthogonal matrix containing null space Z in its first n_{free} - n_{alc} columns and range space Y in its last n_{alc} columns; T is an n_{alc} ×n_{alc} triangular matrix of special form, t_{ij}=0 for i < n-j, where n_{free} is the number of free parameters (n minus the number of active boundary constraints), and n_{alc} is the number of active linear constraints. The Cholesky factor of the projected Hessian matrix Z^{T}_{k}GZ_{k} and the QT decomposition are updated simultaneously when the active set changes.
The LICOMP technique solves a quadratic problem as a linear complementarity problem. It can be used only if G is positive (negative) semi-definite for minimization (maximization) and if the parameters are restricted to be positive.
This technique finds a point that meets the Karush-Kuhn-Tucker conditions by solving the linear complementary problem
The trust-region method uses the gradient g(x^{(k)}) and Hessian matrix G(x^{(k)}) and thus requires that the objective function f(x) have continuous first- and second-order derivatives inside the feasible region.
The trust-region method iteratively optimizes a quadratic approximation to the nonlinear objective function within a hyperelliptic trust region with radius that constrains the step size corresponding to the quality of the quadratic approximation. The trust-region method is implemented using Dennis, Gay, & Welsch (1981), Gay (1983),
The trust region method performs well for small- to medium-sized problems and does not need many function, gradient, and Hessian calls. However, if the computation of the Hessian matrix is computationally expensive, one of the (dual) quasi-Newton or conjugate gradient algorithms may be more efficient.
The NEWRAP technique uses the gradient g(x^{(k)}) and Hessian matrix G(x^{(k)}) and thus requires that the objective function have continuous first- and second-order derivatives inside the feasible region. If second-order derivatives are computed efficiently and precisely the NEWRAP method may perform well for medium-sized to large problems and does not need many function, gradient, and Hessian calls.
This algorithm uses a pure Newton step when the Hessian is positive definite and when the Newton step reduces the value of the objective function successfully. Otherwise a combination of ridging and line-search is done to compute successful steps. If the Hessian is not positive definite, a multiple of the identity matrix is added to the Hessian matrix to make it positive definite (Eskow & Schnabel, 1991).
In each iteration a line search is done along the search direction to find an approximate optimum of the objective function. The default line-search method uses quadratic interpolation and cubic extrapolation (LIS=2).
The NRRIDG technique uses the gradient g(x^{(k)}) and Hessian matrix G(x^{(k)}) and thus requires that the objective function have continuous first- and second-order derivatives inside the feasible region.
This algorithm uses a pure Newton step when the Hessian is positive definite and when the Newton step reduces the value of the objective function successfully. If at least one of these two conditions is not satisfied, a multiple of the identity matrix is added to the Hessian matrix. If this algorithm is used for least-squares problems, it performs a ridged Gauss-Newton minimization.
The NRRIDG method performs well for small to medium-sized problems and does not need many function, gradient, and Hessian calls. However, if the computation of the Hessian matrix is computationally expensive, one of the (dual) quasi-Newton or conjugate gradient algorithms may be more efficient.
Since NRRIDG uses an orthogonal decomposition of the approximate Hessian, each iteration of NRRIDG can be slower than that of NEWRAP which works with Cholesky decomposition. However, usually NRRIDG needs fewer iterations than NEWRAP.
The (dual) quasi-Newton method uses the gradient g(x^{(k)}) and does not need to compute second-order derivatives since they are approximated. It works well for medium to moderately large optimization problems where the objective function and the gradient are much faster to compute than the Hessian but in general requires more iterations than the techniques TRUREG, NEWRAP, and NRRIDG which compute second-order derivatives.
The QUANEW algorithm depends on whether there are nonlinear constraints or not.
headv[cnlpfunorlin]Unconstrained or Linearly Constrained Problems
If there are no nonlinear constraints QUANEW is either
Four update formulas can be specified with the UPDATE= option:
In each iteration a line search is done along the search direction to find an approximate optimum. The default line-search method uses quadratic interpolation and cubic extrapolation to obtain a step size satisfying the Goldstein conditions. One of the Goldstein conditions can be violated if the feasible region defines an upper limit of the step size. Violating the left side Goldstein condition can affect the positive definiteness of the quasi-Newton update. In those cases either the update is skipped or the iterations are restarted with an identity matrix resulting in the steepest descent or ascent search direction. Line-search algorithms other than the default one can be specified with the LIS= option.
headv[cnlpfconstrained]Nonlinearly Constrained Problems
The algorithm used for nonlinearly constrained quasi-Newton optimization is an efficient modification of Powell's (1978, 1982) Variable Metric Constrained WatchDog (VMCWD) algorithm. A similar but older algorithm (VF02AD) is part of the Harwell library. Both VMCWD and VF02AD use Fletcher's VE02AD algorithm (part of the Harwell library) for positive definite quadratic programming. The PROC NLP QUANEW implementation uses a quadratic programming subroutine that updates and downdates the approximation of the Cholesky factor when the active set changes. The nonlinear QUANEW algorithm is not a feasible point algorithm, and the value of the objective function need not decrease (minimization) or increase (maximization) monotonically. Instead, the algorithm tries to reduce a linear combination of the objective function and constraint violations, called the merit function.
The following are similarities and differences between this algorithm and VMCWD:
which is used in VF02AD. This can be helpful for some applications with linearly dependent active constraints.
Powell (1982), however, it doesn't return automatically after a fixed number of iterations to a former better point. A return here is further delayed if the observed function reduction is close to the expected function reduction of the quadratic model.
The nonlinear QUANEW algorithm needs the Jacobian matrix of the first-order derivatives (constraints normals) of the constraints CJ(x).
You can specify two update formulas with the UPDATE= option:
The double dogleg optimization method combines the ideas of quasi-Newton and trust region methods. The double dogleg algorithm computes in each iteration the step s^{(k)} as the linear combination of the steepest descent or ascent search direction s_{1}^{(k)} and a quasi-Newton search direction s_{2}^{(k)},
The double dogleg optimization technique works well for medium to moderately large optimization problems where the objective function and the gradient are much faster to compute than the Hessian. The implementation is based on Dennis & Mei (1979) and Gay (1983)
but is extended for dealing with boundary and linear constraints. DBLDOG generally needs more iterations than the techniques TRUREG, NEWRAP, or NRRIDG which need second-order derivatives, but each of the DBLDOG iterations is computationally cheap. Furthermore, DBLDOG needs only gradient calls for the update of the Cholesky factor of an approximate Hessian.
Second-order derivatives are not needed by CONGRA and not even approximated. The CONGRA algorithm can be expensive in function and gradient calls but needs only O(n) memory for unconstrained optimization. In general, many iterations are needed to obtain a precise solution, but each of the CONGRA iterations is computationally cheap. Four different update formulas for generating the conjugate directions can be specified using the UPDATE= option:
The default is UPDATE=PB, since it behaved best in most test examples. You are advised to avoid the option UPDATE=CD, which behaved worst in most test examples.
The CONGRA subroutine should be used for optimization problems with large n. For the unconstrained or boundary constrained case, CONGRA needs only O(n) bytes of working memory, whereas all other optimization methods require order O(n^{2}) bytes of working memory. During n successive iterations, uninterrupted by restarts or changes in the working set, the conjugate gradient algorithm computes a cycle of n conjugate search directions. In each iteration, a line search is done along the search direction to find an approximate optimum of the objective function. The default line-search method uses quadratic interpolation and cubic extrapolation to obtain a step size satisfying the Goldstein conditions. One of the Goldstein conditions can be violated if the feasible region defines an upper limit for the step size. Other line-search algorithms can be specified with the LIS= option.
The Nelder-Mead simplex method does not use any derivatives and does not assume that the objective function has continuous derivatives. The objective function itself needs to be continuous. This technique is quite expensive in the number of function calls and may be unable to generate precise results for .
Depending on the kind of constraints, one of the following Nelder-Mead simplex algorithms is used:
headv[cnlpfcoby]Powell's COBYLA Algorithm (COBYLA)
Powell's COBYLA algorithm is a sequential trust-region algorithm (originally with a monotonically decreasing
radius of a spheric trust region) that tries to maintain a regular-shaped simplex over the iterations. A small modification was made to the original algorithm, which permits an increase of the trust-region radius in special situations. A sequence of iterations is performed with a constant trust-region radius until the computed objective function reduction is much less than the predicted reduction. Then, the trust-region radius is reduced. The trust-region radius is increased only if the computed function reduction is relatively close to the predicted reduction and the simplex is well-shaped. The start radius and the final radius can be specified using =INSTEP and =ABSXTOL. The convergence to small values of (high precision) may take many calls of the function and constraint modules and may result in numerical problems. There are two main reasons for the slow convergence of the COBYLA algorithm:
The Levenberg-Marquardt method is a modification of the trust-region method for nonlinear least-squares problems and is implemented as in Mor (1978).
This is the recommended algorithm for small- to medium-sized least-squares problems. Large least-squares problems can be transformed into minimization problems, which can be processed with conjugate gradient or (dual) quasi-Newton techniques. In each iteration LEVMAR solves a quadratically constrained quadratic minimization problem that restricts the step to stay at the surface of or inside an n dimensional elliptical (or spherical) trust region. In each iteration, LEVMAR uses the cross-product Jacobian matrix J^{T}J as an approximate Hessian matrix.
In each iteration of one of the Fletcher & Xu (1987)
(refer also to AlBaali & Fletcher, 1985, 1986)
hybrid quasi-Newton methods, a criterion is used to decide whether a Gauss-Newton or a dual quasi-Newton search direction is appropriate. The VERSION= option can be used to choose one of three criteria (HY1, HY2, HY3) proposed by Fletcher & Xu (1987). The default is VERSION=2; that is, HY2. In each iteration, HYQUAN computes the cross-product Jacobian (used for the Gauss-Newton step), updates the Cholesky factor of an approximate Hessian (used for the quasi-Newton step), and does a line search to compute an approximate minimum along the search direction. The default line-search technique used by HYQUAN is especially designed for least-squares problems (refer to Lindstrm & Wedin, 1984, and AlBaali & Fletcher, 1986). Using the LIS= option you can choose a different line-search algorithm than the default one. Two update formulas can be specified with the UPDATE= option:
The HYQUAN subroutine needs about the same amount of working memory as the LEVMAR algorithm. In most applications LEVMAR seems to be superior to HYQUAN, and using HYQUAN is recommended only when problems are experienced with the performance of LEVMAR.
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