NLopt Algorithms
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===NEWUOA + bound constraints=== | ===NEWUOA + bound constraints=== | ||
+ | This is an algorithm derived from the NEWUOA subroutine of M. J. D. Powell, converted to C and modified for the NLopt stopping criteria. I also modified the code to include a variant, NEWUOA-bound, that permits efficient handling of bound constraints. | ||
+ | |||
+ | The original NEWUOA performs derivative-free unconstrained optimization using an iteratively constructed quadratic approximation for the objective function. See: | ||
+ | |||
+ | * M. J. D. Powell, "The NEWUOA software for unconstrained optimization without derivatives," ''Proc. 40th Workshop on Large Scale Nonlinear Optimization'' (Erice, Italy, 2004). | ||
+ | |||
+ | The original algorithm is specified in NLopt as <code>NLOPT_LN_NEWUOA</code>, and only supports unconstrained problems. For bound constraints, my variant is specified as <code>NLOPT_LN_NEWUOA_BOUND</code>. | ||
+ | |||
+ | In the original NEWUOA algorithm, Powell solved the quadratic subproblems (in routines TRSAPP and BIGLAG) in a spherical trust region via a truncated conjugate-gradient algorithm. In my bound-constrained variant, we use the MMA algorithm for these subproblems to solve them with both bound constraints and a spherical trust region. In principle, we should also change the BIGDEN subroutine in a similar way (since BIGDEN also approximately solves a trust-region subproblem), but instead I just truncated its result to the bounds (which probably gives suboptimal | ||
+ | convergence, but BIGDEN is called only very rarely in practice). | ||
===PRAXIS (PRincipal AXIS)=== | ===PRAXIS (PRincipal AXIS)=== |
Revision as of 05:24, 12 November 2008
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NLopt includes implementations of a number of different optimization algorithms. These algorithms are listed below, including links to the original source code (if any) and citations to the relevant articles in the literature (see Citing NLopt).
Even where I found available free/open-source code for the various algorithms, I modified the code at least slightly (and in some cases noted below, substantially) for inclusion into NLopt. I apologize in advance to the authors for any new bugs I may have inadvertantly introduced into their code.
Contents |
Nomenclature
Each algorithm in NLopt is identified by a named constant, which is passed to the NLopt routines in the various languages in order to select a particular algorithm. These constants are of the form NLOPT_{G,L}{N,D}_xxxx
, where G
/L
denotes global/local optimization and N
/D
denotes derivative-free/gradient-based algorithms, respectively.
For example, the NLOPT_LN_COBYLA
constant refers to the COBYLA algorithm (described below), which is a local (L
) derivative-free (N
) optimization algorithm.
Many of the algorithms have several variants, which are grouped together below.
Global optimization
All of the global-optimization algorithms currently require you to specify bound constraints on all the optimization parameters.
DIRECT and DIRECT-L
DIRECT is the DIviding RECTangles algorithm for global optimization, described in:
- D. R. Jones, C. D. Perttunen, and B. E. Stuckmann, "Lipschitzian optimization without the lipschitz constant," J. Optimization Theory and Applications, vol. 79, p. 157 (1993).
and DIRECT-L is the "locally biased" variant proposed by:
- J. M. Gablonsky and C. T. Kelley, "A locally-biased form of the DIRECT algorithm," J. Global Optimization, vol. 21 (1), p. 27-37 (2001).
These is are deterministic-search algorithms based on systematic division of the search domain into smaller and smaller hyperrectangles. The Gablonsky version makes the algorithm "more biased towards local search" so that it
is more efficient for functions without too many local minima. NLopt contains several implementations of both of these algorithms. I would tend to try NLOPT_GN_DIRECT_L
first; YMMV.
First, it contains a from-scratch re-implementation of both algorithms, specified by the constants NLOPT_GN_DIRECT
and NLOPT_GN_DIRECT_L
, respectively.
Second, there is a slightly randomized variant of DIRECT-L, specified by NLOPT_GLOBAL_DIRECT_L_RAND
, which uses some randomization to help decide which dimension to halve next in the case of near-ties.
The DIRECT and DIRECT-L algorithms start by rescaling the bound constraints to a hypercube, which gives all dimensions equal weight in the search procedure. If your dimensions do not have equal weight, e.g. if you have a "long and skinny" search space and your function varies at about the same speed in all directions, it may be better to use unscaled variants of these algorthms, which are specified as NLOPT_GLOBAL_DIRECT_NOSCAL
, NLOPT_GLOBAL_DIRECT_L_NOSCAL
, and NLOPT_GLOBAL_DIRECT_L_RAND_NOSCAL
, respectively. These implementations have a number of hard-coded limitations on things like the number of function evaluations; I removed several of these limitations, but some remain.
Finally, NLopt also includes separate implementations based on the original Fortran code by Gablonsky et al. (1998-2001), which are specified as NLOPT_GN_ORIG_DIRECT
and NLOPT_GN_ORIG_DIRECT_L
.
Most of the above algorithms only handle bound constraints, and in fact require finite bound constraints (they are not applicable to unconstrained problems). They do not handle arbitrary nonlinear constraints. However, the ORIG
versions by Gablonsky et al. include some support for arbitrary constraints, if you represent constraint violations by having your objective function return NaN or infinity (HUGE_VAL
, in C).
Controlled Random Search (CRS) with local mutation
My implementation of the "controlled random search" (CRS) algorithm (in particular, the CRS2 variant) with the "local mutation" modification, as defined by:
- P. Kaelo and M. M. Ali, "Some variants of the controlled random search algorithm for global optimization," J. Optim. Theory Appl. 130 (2), 253-264 (2006).
The original CRS2 algorithm was described by:
- W. L. Price, "A controlled random search procedure for global optimization," in Towards Global Optimization 2, p. 71-84 edited by L. C. W. Dixon and G. P. Szego (North-Holland Press, Amsterdam, 1978).
The CRS algorithms are sometimes compared to genetic algorithms, in that they start with a random "population" of points, and randomly "evolve" these points by heuristic rules. In this case, the "evolution" somewhat resembles a randomized Nelder-Mead algorithm.
Only bound-constrained problems are supported by this algorithm.
Multi-level single-linkage (MLSL)
This is my implementation of the "Multi-Level Single-Linkage" (MLSL) algorithm for global optimization by a sequence random local optimizations, proposed by:
- A. H. G. Rinnooy Kan and G. T. Timmer, "Stochastic global optimization methods," Mathematical Programming, vol. 39, p. 27-78 (1987). (Actually 2 papers — part I: clustering methods, p. 27, then part II: multilevel methods, p. 57.)
We also include a modification of MLSL use a Sobol low-discrepancy sequence (LDS) instead of pseudorandom numbers, which was argued to improve the convergence rate by:
- Sergei Kucherenko and Yury Sytsko, "Application of deterministic low-discrepancy sequences in global optimization," Computational Optimization and Applications, vol. 30, p. 297-318 (2005).
In either case, MLSL is a "multistart" algorithm: it works by doing a sequence of local optimizations (using some other local optimization algorithm) from random or low-discrepancy starting points. MLSL is distinguished, however by a "clustering" heuristic that helps it to avoid repeated searches of the same local optima, and has some theoretical guarantees of finding all local optima in a finite number of local minimizations.
The local-search portion of MLSL can use any of the other algorithms in NLopt, and in particular can use either gradient-based (D
) or derivative-free algorithms (N
) The local search uses the derivative/nonderivative algorithm set by nlopt_set_local_search_algorithm (currently defaulting to NLOPT_LD_MMA
and NLOPT_LN_COBYLA
for derivative/nonderivative searches, respectively).
LDS-based MLSL with gradient-based local search is specified as NLOPT_GD_MLSL_LDS
, while LDS-based MLSL with derivative-free local search is specified by NLOPT_GN_MLSL_LDS
. The non-LDS original MLSL (using pseudo-random numbers, currently via the Mersenne twister algorithm) is indicated by NLOPT_GD_MLSL
and NLOPT_GN_MLSL
, respectively.
StoGO
This is an algorithm adapted from the code downloaded from
by Madsen et al. StoGO is a global optimization algorithm that works by systematically dividing the search space (which must be bound-constrained) into smaller hyper-rectangles via a branch-and-bound technique, and searching them by a gradient-based local-search algorithm (a BFGS variant), optionally including some randomness (hence the "Sto", which stands for "stochastic" I believe).
StoGO is written in C++, which means that it is only included when you compile the [[NLopt Installation#NLopt with C++ algorithms|C++ algorithms]] enabled.
StoGO is specified within NLopt by NLOPT_GD_STOGO
, or NLOPT_GD_STOGO_RAND
for the randomized variant.
Some referenes on StoGO are:
- S. Gudmundsson, "Parallel Global Optimization," M.Sc. Thesis, IMM, Technical University of Denmark, 1998.
- K. Madsen, S. Zertchaninov, and A. Zilinskas, "Global Optimization using Branch-and-Bound," unpublished (1998). A preprint of this paper is included in the
stogo
subdirectory of NLopt aspaper.pdf
. - S. Zertchaninov and K. Madsen, "A C++ Programme for Global Optimization," IMM-REP-1998-04, Department of Mathematical Modelling, Technical University of Denmark, DK-2800 Lyngby, Denmark, 1998. A copy of this report is included in the
stogo
subdirectory of NLopt astechreport.pdf
.
Local derivative-free optimization
Of these algorithms, only COBYLA currently supports arbitrary nonlinear inequality constraints; the rest of them support bound-constrained or unconstrained problems only.
COBYLA (Constrained Optimization BY Linear Approximations)
This is a derivative of Powell's implementation of the COBYLA (Constrained Optimization BY Linear Approximations) algorithm for derivative-free optimization with nonlinear inequality constraints, by M. J. D. Powell, described in:
- M. J. D. Powell, "A direct search optimization method that models the objective and constraint functions by linear interpolation," in Advances in Optimization and Numerical Analysis, eds. S. Gomez and J.-P. Hennart (Kluwer Academic: Dordrecht, 1994), p. 51-67.
and reviewed in:
- M. J. D. Powell, "Direct search algorithms for optimization calculations," Acta Numerica 7, 287-336 (1998).
It constructs successive linear approximations of the objective function and constraints via a simplex of n+1 points (in n dimensions), and optimizes these approximations in a trust region at each step.
The original code itself was written in Fortran by Powell and was converted to C in 2004 by Jean-Sebastien Roy (js@jeannot.org) for the SciPy project. The version in NLopt was based on Roy's C version, downloaded from:
and slightly modified (e.g. to incorporate all the NLopt termination criteria).
It is specified within NLopt as NLOPT_LN_COBYLA
.
NEWUOA + bound constraints
This is an algorithm derived from the NEWUOA subroutine of M. J. D. Powell, converted to C and modified for the NLopt stopping criteria. I also modified the code to include a variant, NEWUOA-bound, that permits efficient handling of bound constraints.
The original NEWUOA performs derivative-free unconstrained optimization using an iteratively constructed quadratic approximation for the objective function. See:
- M. J. D. Powell, "The NEWUOA software for unconstrained optimization without derivatives," Proc. 40th Workshop on Large Scale Nonlinear Optimization (Erice, Italy, 2004).
The original algorithm is specified in NLopt as NLOPT_LN_NEWUOA
, and only supports unconstrained problems. For bound constraints, my variant is specified as NLOPT_LN_NEWUOA_BOUND
.
In the original NEWUOA algorithm, Powell solved the quadratic subproblems (in routines TRSAPP and BIGLAG) in a spherical trust region via a truncated conjugate-gradient algorithm. In my bound-constrained variant, we use the MMA algorithm for these subproblems to solve them with both bound constraints and a spherical trust region. In principle, we should also change the BIGDEN subroutine in a similar way (since BIGDEN also approximately solves a trust-region subproblem), but instead I just truncated its result to the bounds (which probably gives suboptimal convergence, but BIGDEN is called only very rarely in practice).