Decision Tree for Optimization Software

### The Unconstrained NLO Problem

The Problem: min f(x), n=dim(x)

The picture shows the level set representation of Beale's testfunction

f(x,y)=(1.5-x(1-y))^2+(2.25-x(1-y^2))^2+(2.625-x(1-y^3))^2.
It has a unique global minimizer at (3,0.5) and a saddle point at (0,0). There is a further saddle point at (0.100538,-2.644514). For x=0 or y=1 it is constant with value 14.203125. In x<0 there exists an arc y(x)>0, where f decreases monotonically as x-> -INF. Initial values near x=0 for large |y| or x<0, y>0 will let descent methods fail.

The methods listed here all restrict themselves to finding one solution of grad f(x)=0. For trust region based methods one often can show that this solution automatically satisfies the second order necessary conditions.

#### f is a "general" twice continuously differentiable function, but gradient is not available:

The methods implicitly all assume that f is twice continuously differentiable. They may fail or converge very slowly if this is not the case. Read M. Powell's and M. Wright's papers on Direct Search methods as well as M. Powell's paper on optimization without derivatives.

 FMIN Brent's direct search method, one variable. Java version NEWUOA Powell's new unconstrained optimization by quadratic approximation (f77) BOBYQA Powell's bound-constrained optimization by quadratic approximation (f77) C#-version quasi-Newton method by Schnabel et al (f77) C++ version Java version MWD Trust region method with approximations of gradient and Hessian, bound constraints (Matlab) WEDGE Interpolation/trust region method for moderate dimensions(Matlab) netlib/opt/praxis f depends on few variables, the "principal axis method", tries to approximate the curvature of f MCToolbox three direct search methods incl Nelder-Mead, Matlab nelmead f depends on few variables, Nelder-Mead simplex-search method (no sound theoretical basis), f77 netlib/opt/subplex f depends on few variables, modification of the Nelder-Mead simplex-search method (no sound theoretical basis), (Matlab version) fminsi modified Nelder-Mead, LGPL-licensed, source, testdriver DFO derivative free method of Conn, Scheinberg, and Toint Asynchronous and Fault Tolerant Parallel Pattern Search (LGLP, C++, serial or PVM or MPI)

#### f is only Lipschitz continuous or less: n not too large

 GradSamp Nonsmooth, nonconvex optimization by gradient sampling, by M. Overton et al; needs one of three QP solvers (Matlab) HANSO Hybrid algorithm for nonsmooth, nonconvex optimization using quasi-Newton updating, bundling and gradient sampling (Matlab) SolvOpt Solves nonsmooth unconstrained and constrained problems of moderate dimensions. Matlab m-files, Fortran and C source available. OBOE Oracle-Based Optimization Engine for convex problems, uses Proximal-ACCPM interior point method, C++ NDA Four different routines for Lipschitz continuous functions and for (small) minimax problems OpenOpt Python package, also general constraints NSO various software for large-scale nonsmooth optimization (Fortran) VXQR1 Derivative-free unconstrained optimization based on QR factorizations (Matlab)

#### The gradient of f is available or finite difference approximations are applicable :

n not too large (= some hundred say)
 domin Spellucci's implementation of BFGS the Shanno-Phua version of BFGS plus CG trust region BFGS, this is a reliable and fairly efficient code. tensor model quasi-Newton method, may be better for strongly nonquadratic f trust-region Newton, requires gradient several methods, paper (Matlab) A new conjugate gradient method with guaranteed descent (f77/C) BFGS with some robustness against nonsmoothness (Matlab)

#### f is convex differentiable, n large:

 netlib/opt/ve08 f is a sum of convex differentiable functions each of which has considerably less variables than n (partially separable problem)(also for bound constraints), special quasi Newton method

#### n large, f general, but not too wildly behaved :

 A combined limited memory qN/cg method (degrades for nonconvex f, nevertheless applicable; for ill-conditioned problem an individual preconditioner must be provided) MINPACK-2 A limited memory quasi Newton method. Directories contain software, drivers and manuals. (dcsrch.f, the step-size-algorithm, must be added; to be found e.g. in the following code) a limited memory quasi Newton method using a new matrix representation; directory contains software, drivers and a user's manual. Not to be recommended for ill-conditioned problems. Add your own preconditioner! needs C++ and Fortran compiler updated version of basic algorithm (C#, C++, C++-MP, Delphi, VB) three basic cg methods, Matlab toolbox preconditioning package LM Several limited memory f77-routines for unconstrained and box-constrained optimization; also for least squares

#### n large, f strongly nonquadratic :

 Nash's truncated Newton method based on the Lanczos method, without explicit eigenvalue computation. takes directions of negative curvature into account. netlib/toms/702 truncated Newton based on the Lanczos process TRON trust region Newton, preconditioned cg, also for bound constraints PL2 truncated Newton method using the Lanczos process with direct computation of a truncated spectral decomposition, uses a so called three directions method from Heinrich, uses directions of negative curvature. for large dimensional problems.

#### f noisy :

 CMA-ES Evolution Strategy with Covariance Matrix Adaptation (Matlab) SNOBFIT Stable Noisy Optimization by Branch and FIT (Matlab) IMFIL implicit filtering subject to box constraints and with multiple minima (Matlab) DIRECT the DIRECT algorithm (in Matlab) VTDIRECT95 Serial and Parallel Codes for the Global Optimization Algorithm DIRECT (Fortran 95) netlib/opt/praxis often applied to noisy problems although not directly designed for such Powell's new unconstrained optimization by quadratic approximation (f77) BOBYQA Powell's bound-constrained optimization by quadratic approximation (f77) netlib/opt/subplex f depends on few variables, modification of the Nelder-Mead simplex-search method (no sound theoretical basis), (Matlab version)

#### f expensive to evaluate :

 RBFOpt radial basis function library for black box (derivative free) optimization of functions with costly function evaluation (Python) Scatter Search evolutionary method, see here, but claims to use considerably less evaluations; example code for linear ordering (C) SPACE global optimization through meta-modeling: fitting, cross-validation, prediction, minimization etc (C++) Spacemap Optimization using Surrogate Models (Matlab) STK Matlab/Octave toolbox to construct a kriging approximation (surrogate) to computer models DACE Matlab toolbox to construct a kriging approximation (surrogate) to computer models

#### f complex :

 Complex Optimization Toolbox MATLAB toolbox for optimization of complex variables