1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
5 // Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
6 //
7 // The algorithm of this class initially comes from MINPACK whose original authors are:
8 // Copyright Jorge More - Argonne National Laboratory
9 // Copyright Burt Garbow - Argonne National Laboratory
10 // Copyright Ken Hillstrom - Argonne National Laboratory
11 //
12 // This Source Code Form is subject to the terms of the Minpack license
13 // (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
14 //
15 // This Source Code Form is subject to the terms of the Mozilla
16 // Public License v. 2.0. If a copy of the MPL was not distributed
17 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
18
19 #ifndef EIGEN_LEVENBERGMARQUARDT_H
20 #define EIGEN_LEVENBERGMARQUARDT_H
21
22
23 namespace Eigen {
24 namespace LevenbergMarquardtSpace {
25 enum Status {
26 NotStarted = -2,
27 Running = -1,
28 ImproperInputParameters = 0,
29 RelativeReductionTooSmall = 1,
30 RelativeErrorTooSmall = 2,
31 RelativeErrorAndReductionTooSmall = 3,
32 CosinusTooSmall = 4,
33 TooManyFunctionEvaluation = 5,
34 FtolTooSmall = 6,
35 XtolTooSmall = 7,
36 GtolTooSmall = 8,
37 UserAsked = 9
38 };
39 }
40
41 template <typename _Scalar, int NX=Dynamic, int NY=Dynamic>
42 struct DenseFunctor
43 {
44 typedef _Scalar Scalar;
45 enum {
46 InputsAtCompileTime = NX,
47 ValuesAtCompileTime = NY
48 };
49 typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
50 typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
51 typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
52 typedef ColPivHouseholderQR<JacobianType> QRSolver;
53 const int m_inputs, m_values;
54
DenseFunctorDenseFunctor55 DenseFunctor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
DenseFunctorDenseFunctor56 DenseFunctor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
57
inputsDenseFunctor58 int inputs() const { return m_inputs; }
valuesDenseFunctor59 int values() const { return m_values; }
60
61 //int operator()(const InputType &x, ValueType& fvec) { }
62 // should be defined in derived classes
63
64 //int df(const InputType &x, JacobianType& fjac) { }
65 // should be defined in derived classes
66 };
67
68 template <typename _Scalar, typename _Index>
69 struct SparseFunctor
70 {
71 typedef _Scalar Scalar;
72 typedef _Index Index;
73 typedef Matrix<Scalar,Dynamic,1> InputType;
74 typedef Matrix<Scalar,Dynamic,1> ValueType;
75 typedef SparseMatrix<Scalar, ColMajor, Index> JacobianType;
76 typedef SparseQR<JacobianType, COLAMDOrdering<int> > QRSolver;
77 enum {
78 InputsAtCompileTime = Dynamic,
79 ValuesAtCompileTime = Dynamic
80 };
81
SparseFunctorSparseFunctor82 SparseFunctor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
83
inputsSparseFunctor84 int inputs() const { return m_inputs; }
valuesSparseFunctor85 int values() const { return m_values; }
86
87 const int m_inputs, m_values;
88 //int operator()(const InputType &x, ValueType& fvec) { }
89 // to be defined in the functor
90
91 //int df(const InputType &x, JacobianType& fjac) { }
92 // to be defined in the functor if no automatic differentiation
93
94 };
95 namespace internal {
96 template <typename QRSolver, typename VectorType>
97 void lmpar2(const QRSolver &qr, const VectorType &diag, const VectorType &qtb,
98 typename VectorType::Scalar m_delta, typename VectorType::Scalar &par,
99 VectorType &x);
100 }
101 /**
102 * \ingroup NonLinearOptimization_Module
103 * \brief Performs non linear optimization over a non-linear function,
104 * using a variant of the Levenberg Marquardt algorithm.
105 *
106 * Check wikipedia for more information.
107 * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
108 */
109 template<typename _FunctorType>
110 class LevenbergMarquardt : internal::no_assignment_operator
111 {
112 public:
113 typedef _FunctorType FunctorType;
114 typedef typename FunctorType::QRSolver QRSolver;
115 typedef typename FunctorType::JacobianType JacobianType;
116 typedef typename JacobianType::Scalar Scalar;
117 typedef typename JacobianType::RealScalar RealScalar;
118 typedef typename QRSolver::StorageIndex PermIndex;
119 typedef Matrix<Scalar,Dynamic,1> FVectorType;
120 typedef PermutationMatrix<Dynamic,Dynamic> PermutationType;
121 public:
LevenbergMarquardt(FunctorType & functor)122 LevenbergMarquardt(FunctorType& functor)
123 : m_functor(functor),m_nfev(0),m_njev(0),m_fnorm(0.0),m_gnorm(0),
124 m_isInitialized(false),m_info(InvalidInput)
125 {
126 resetParameters();
127 m_useExternalScaling=false;
128 }
129
130 LevenbergMarquardtSpace::Status minimize(FVectorType &x);
131 LevenbergMarquardtSpace::Status minimizeInit(FVectorType &x);
132 LevenbergMarquardtSpace::Status minimizeOneStep(FVectorType &x);
133 LevenbergMarquardtSpace::Status lmder1(
134 FVectorType &x,
135 const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
136 );
137 static LevenbergMarquardtSpace::Status lmdif1(
138 FunctorType &functor,
139 FVectorType &x,
140 Index *nfev,
141 const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
142 );
143
144 /** Sets the default parameters */
resetParameters()145 void resetParameters()
146 {
147 using std::sqrt;
148
149 m_factor = 100.;
150 m_maxfev = 400;
151 m_ftol = sqrt(NumTraits<RealScalar>::epsilon());
152 m_xtol = sqrt(NumTraits<RealScalar>::epsilon());
153 m_gtol = 0. ;
154 m_epsfcn = 0. ;
155 }
156
157 /** Sets the tolerance for the norm of the solution vector*/
setXtol(RealScalar xtol)158 void setXtol(RealScalar xtol) { m_xtol = xtol; }
159
160 /** Sets the tolerance for the norm of the vector function*/
setFtol(RealScalar ftol)161 void setFtol(RealScalar ftol) { m_ftol = ftol; }
162
163 /** Sets the tolerance for the norm of the gradient of the error vector*/
setGtol(RealScalar gtol)164 void setGtol(RealScalar gtol) { m_gtol = gtol; }
165
166 /** Sets the step bound for the diagonal shift */
setFactor(RealScalar factor)167 void setFactor(RealScalar factor) { m_factor = factor; }
168
169 /** Sets the error precision */
setEpsilon(RealScalar epsfcn)170 void setEpsilon (RealScalar epsfcn) { m_epsfcn = epsfcn; }
171
172 /** Sets the maximum number of function evaluation */
setMaxfev(Index maxfev)173 void setMaxfev(Index maxfev) {m_maxfev = maxfev; }
174
175 /** Use an external Scaling. If set to true, pass a nonzero diagonal to diag() */
setExternalScaling(bool value)176 void setExternalScaling(bool value) {m_useExternalScaling = value; }
177
178 /** \returns the tolerance for the norm of the solution vector */
xtol()179 RealScalar xtol() const {return m_xtol; }
180
181 /** \returns the tolerance for the norm of the vector function */
ftol()182 RealScalar ftol() const {return m_ftol; }
183
184 /** \returns the tolerance for the norm of the gradient of the error vector */
gtol()185 RealScalar gtol() const {return m_gtol; }
186
187 /** \returns the step bound for the diagonal shift */
factor()188 RealScalar factor() const {return m_factor; }
189
190 /** \returns the error precision */
epsilon()191 RealScalar epsilon() const {return m_epsfcn; }
192
193 /** \returns the maximum number of function evaluation */
maxfev()194 Index maxfev() const {return m_maxfev; }
195
196 /** \returns a reference to the diagonal of the jacobian */
diag()197 FVectorType& diag() {return m_diag; }
198
199 /** \returns the number of iterations performed */
iterations()200 Index iterations() { return m_iter; }
201
202 /** \returns the number of functions evaluation */
nfev()203 Index nfev() { return m_nfev; }
204
205 /** \returns the number of jacobian evaluation */
njev()206 Index njev() { return m_njev; }
207
208 /** \returns the norm of current vector function */
fnorm()209 RealScalar fnorm() {return m_fnorm; }
210
211 /** \returns the norm of the gradient of the error */
gnorm()212 RealScalar gnorm() {return m_gnorm; }
213
214 /** \returns the LevenbergMarquardt parameter */
lm_param(void)215 RealScalar lm_param(void) { return m_par; }
216
217 /** \returns a reference to the current vector function
218 */
fvec()219 FVectorType& fvec() {return m_fvec; }
220
221 /** \returns a reference to the matrix where the current Jacobian matrix is stored
222 */
jacobian()223 JacobianType& jacobian() {return m_fjac; }
224
225 /** \returns a reference to the triangular matrix R from the QR of the jacobian matrix.
226 * \sa jacobian()
227 */
matrixR()228 JacobianType& matrixR() {return m_rfactor; }
229
230 /** the permutation used in the QR factorization
231 */
permutation()232 PermutationType permutation() {return m_permutation; }
233
234 /**
235 * \brief Reports whether the minimization was successful
236 * \returns \c Success if the minimization was succesful,
237 * \c NumericalIssue if a numerical problem arises during the
238 * minimization process, for exemple during the QR factorization
239 * \c NoConvergence if the minimization did not converge after
240 * the maximum number of function evaluation allowed
241 * \c InvalidInput if the input matrix is invalid
242 */
info()243 ComputationInfo info() const
244 {
245
246 return m_info;
247 }
248 private:
249 JacobianType m_fjac;
250 JacobianType m_rfactor; // The triangular matrix R from the QR of the jacobian matrix m_fjac
251 FunctorType &m_functor;
252 FVectorType m_fvec, m_qtf, m_diag;
253 Index n;
254 Index m;
255 Index m_nfev;
256 Index m_njev;
257 RealScalar m_fnorm; // Norm of the current vector function
258 RealScalar m_gnorm; //Norm of the gradient of the error
259 RealScalar m_factor; //
260 Index m_maxfev; // Maximum number of function evaluation
261 RealScalar m_ftol; //Tolerance in the norm of the vector function
262 RealScalar m_xtol; //
263 RealScalar m_gtol; //tolerance of the norm of the error gradient
264 RealScalar m_epsfcn; //
265 Index m_iter; // Number of iterations performed
266 RealScalar m_delta;
267 bool m_useExternalScaling;
268 PermutationType m_permutation;
269 FVectorType m_wa1, m_wa2, m_wa3, m_wa4; //Temporary vectors
270 RealScalar m_par;
271 bool m_isInitialized; // Check whether the minimization step has been called
272 ComputationInfo m_info;
273 };
274
275 template<typename FunctorType>
276 LevenbergMarquardtSpace::Status
minimize(FVectorType & x)277 LevenbergMarquardt<FunctorType>::minimize(FVectorType &x)
278 {
279 LevenbergMarquardtSpace::Status status = minimizeInit(x);
280 if (status==LevenbergMarquardtSpace::ImproperInputParameters) {
281 m_isInitialized = true;
282 return status;
283 }
284 do {
285 // std::cout << " uv " << x.transpose() << "\n";
286 status = minimizeOneStep(x);
287 } while (status==LevenbergMarquardtSpace::Running);
288 m_isInitialized = true;
289 return status;
290 }
291
292 template<typename FunctorType>
293 LevenbergMarquardtSpace::Status
minimizeInit(FVectorType & x)294 LevenbergMarquardt<FunctorType>::minimizeInit(FVectorType &x)
295 {
296 n = x.size();
297 m = m_functor.values();
298
299 m_wa1.resize(n); m_wa2.resize(n); m_wa3.resize(n);
300 m_wa4.resize(m);
301 m_fvec.resize(m);
302 //FIXME Sparse Case : Allocate space for the jacobian
303 m_fjac.resize(m, n);
304 // m_fjac.reserve(VectorXi::Constant(n,5)); // FIXME Find a better alternative
305 if (!m_useExternalScaling)
306 m_diag.resize(n);
307 eigen_assert( (!m_useExternalScaling || m_diag.size()==n) && "When m_useExternalScaling is set, the caller must provide a valid 'm_diag'");
308 m_qtf.resize(n);
309
310 /* Function Body */
311 m_nfev = 0;
312 m_njev = 0;
313
314 /* check the input parameters for errors. */
315 if (n <= 0 || m < n || m_ftol < 0. || m_xtol < 0. || m_gtol < 0. || m_maxfev <= 0 || m_factor <= 0.){
316 m_info = InvalidInput;
317 return LevenbergMarquardtSpace::ImproperInputParameters;
318 }
319
320 if (m_useExternalScaling)
321 for (Index j = 0; j < n; ++j)
322 if (m_diag[j] <= 0.)
323 {
324 m_info = InvalidInput;
325 return LevenbergMarquardtSpace::ImproperInputParameters;
326 }
327
328 /* evaluate the function at the starting point */
329 /* and calculate its norm. */
330 m_nfev = 1;
331 if ( m_functor(x, m_fvec) < 0)
332 return LevenbergMarquardtSpace::UserAsked;
333 m_fnorm = m_fvec.stableNorm();
334
335 /* initialize levenberg-marquardt parameter and iteration counter. */
336 m_par = 0.;
337 m_iter = 1;
338
339 return LevenbergMarquardtSpace::NotStarted;
340 }
341
342 template<typename FunctorType>
343 LevenbergMarquardtSpace::Status
lmder1(FVectorType & x,const Scalar tol)344 LevenbergMarquardt<FunctorType>::lmder1(
345 FVectorType &x,
346 const Scalar tol
347 )
348 {
349 n = x.size();
350 m = m_functor.values();
351
352 /* check the input parameters for errors. */
353 if (n <= 0 || m < n || tol < 0.)
354 return LevenbergMarquardtSpace::ImproperInputParameters;
355
356 resetParameters();
357 m_ftol = tol;
358 m_xtol = tol;
359 m_maxfev = 100*(n+1);
360
361 return minimize(x);
362 }
363
364
365 template<typename FunctorType>
366 LevenbergMarquardtSpace::Status
lmdif1(FunctorType & functor,FVectorType & x,Index * nfev,const Scalar tol)367 LevenbergMarquardt<FunctorType>::lmdif1(
368 FunctorType &functor,
369 FVectorType &x,
370 Index *nfev,
371 const Scalar tol
372 )
373 {
374 Index n = x.size();
375 Index m = functor.values();
376
377 /* check the input parameters for errors. */
378 if (n <= 0 || m < n || tol < 0.)
379 return LevenbergMarquardtSpace::ImproperInputParameters;
380
381 NumericalDiff<FunctorType> numDiff(functor);
382 // embedded LevenbergMarquardt
383 LevenbergMarquardt<NumericalDiff<FunctorType> > lm(numDiff);
384 lm.setFtol(tol);
385 lm.setXtol(tol);
386 lm.setMaxfev(200*(n+1));
387
388 LevenbergMarquardtSpace::Status info = LevenbergMarquardtSpace::Status(lm.minimize(x));
389 if (nfev)
390 * nfev = lm.nfev();
391 return info;
392 }
393
394 } // end namespace Eigen
395
396 #endif // EIGEN_LEVENBERGMARQUARDT_H
397