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OptimizerRep.h
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1 #ifndef SimTK_SIMMATH_OPTIMIZER_REP_H_
2 #define SimTK_SIMMATH_OPTIMIZER_REP_H_
3 
4 /* -------------------------------------------------------------------------- *
5  * Simbody(tm): SimTKmath *
6  * -------------------------------------------------------------------------- *
7  * This is part of the SimTK biosimulation toolkit originating from *
8  * Simbios, the NIH National Center for Physics-Based Simulation of *
9  * Biological Structures at Stanford, funded under the NIH Roadmap for *
10  * Medical Research, grant U54 GM072970. See https://simtk.org/home/simbody. *
11  * *
12  * Portions copyright (c) 2006-13 Stanford University and the Authors. *
13  * Authors: Jack Middleton *
14  * Contributors: Michael Sherman *
15  * *
16  * Licensed under the Apache License, Version 2.0 (the "License"); you may *
17  * not use this file except in compliance with the License. You may obtain a *
18  * copy of the License at http://www.apache.org/licenses/LICENSE-2.0. *
19  * *
20  * Unless required by applicable law or agreed to in writing, software *
21  * distributed under the License is distributed on an "AS IS" BASIS, *
22  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. *
23  * See the License for the specific language governing permissions and *
24  * limitations under the License. *
25  * -------------------------------------------------------------------------- */
26 
27 #include "SimTKcommon.h"
28 #include "simmath/Optimizer.h"
29 #include "simmath/Differentiator.h"
30 #include <map>
31 
32 namespace SimTK {
33 
34 
35 /* class for Diff jacobian */
37 public:
38  SysObjectiveFunc(int ny, const OptimizerSystem* sysPtr )
39  : Differentiator::GradientFunction(ny) { sysp = sysPtr; }
40 
41  // Must provide this pure virtual function.
42  int f(const Vector& y, Real& fy) const {
43  return(sysp->objectiveFunc(y, true, fy)); // class user's objectiveFunc
44  }
46 };
47 
48 
49 /* class for Diff gradient */
51  public:
52  SysConstraintFunc(int nf, int ny, const OptimizerSystem* sysPtr)
53  : Differentiator::JacobianFunction(nf,ny) { sysp = sysPtr; }
54 
55  // Must provide this pure virtual function.
56  int f(const Vector& y, Vector& fy) const {
57  return(sysp->constraintFunc(y, true, fy)); // calls user's contraintFunc
58  }
60 };
61 
62 
64 public:
65  virtual ~OptimizerRep();
67  : sysp(&sys),
68  myHandle(0),
69  cf(0),
70  of(0),
71  jacDiff(0),
72  gradDiff(0),
73  convergenceTolerance(Real(1e-3)),
74  constraintTolerance(Real(1e-4)),
75  maxIterations(1000),
76  limitedMemoryHistory(50),
77  diagnosticsLevel(0),
78  diffMethod(Differentiator::CentralDifference),
79  objectiveEstimatedAccuracy(SignificantReal),
80  constraintsEstimatedAccuracy(SignificantReal),
81  numericalGradient(false),
82  numericalJacobian(false)
83 
84  {
85  }
87  : sysp(0),
88  myHandle(0),
89  cf(0),
90  of(0),
91  jacDiff(0),
92  gradDiff(0),
93  convergenceTolerance(Real(1e-3)),
94  constraintTolerance(Real(1e-4)),
95  maxIterations(1000),
96  limitedMemoryHistory(50),
97  diagnosticsLevel(0),
98  diffMethod(Differentiator::CentralDifference),
99  objectiveEstimatedAccuracy(SignificantReal),
100  constraintsEstimatedAccuracy(SignificantReal),
101  numericalGradient(false),
102  numericalJacobian(false)
103  {
104  }
105 
106  virtual OptimizerRep* clone() const { return 0; };
107  static bool isAvailable() { return true; }
108 
109  virtual Real optimize( Vector &results ) = 0;
110 
111  const OptimizerSystem& getOptimizerSystem() const {return *sysp;}
112 
113 
114  void setDiagnosticsLevel( const int level );
115  void setConvergenceTolerance( Real accuracy );
116  void setConstraintTolerance( Real tolerance );
117  void setMaxIterations( const int iter );
118  void setLimitedMemoryHistory( const int history );
119 
120  bool setAdvancedStrOption( const std::string &option, const std::string &value );
121  bool setAdvancedRealOption( const std::string &option, const Real value );
122  bool setAdvancedIntOption( const std::string &option, const int value );
123  bool setAdvancedBoolOption( const std::string &option, const bool value );
124 
125  bool getAdvancedStrOption( const std::string &option, std::string &value ) const;
126  bool getAdvancedRealOption( const std::string &option, Real &value ) const;
127  bool getAdvancedIntOption( const std::string &option, int &value ) const;
128  bool getAdvancedBoolOption( const std::string &option, bool &value ) const;
129 
130  void setMyHandle(Optimizer& cp) {myHandle = &cp;}
131  const Optimizer& getMyHandle() const {assert(myHandle); return *myHandle;}
132  void clearMyHandle() {myHandle=0;}
133 
134  void useNumericalGradient(bool flag, Real objEstAccuracy);
135  void useNumericalJacobian(bool flag, Real consEstAccuracy);
136  void setDifferentiatorMethod( Differentiator::Method method);
137 
138  bool isUsingNumericalGradient() const { return numericalGradient; }
139  bool isUsingNumericalJacobian() const { return numericalJacobian; }
140  Differentiator::Method getDifferentiatorMethod() const {return diffMethod;}
142  { return objectiveEstimatedAccuracy; }
144  { return constraintsEstimatedAccuracy; }
145 
147  assert(gradDiff);
148  return *gradDiff;
149  }
150 
152  assert(jacDiff);
153  return *jacDiff;
154  }
155 
158  }
159 
160  static int numericalGradient_static( const OptimizerSystem&, const Vector & parameters, const bool new_parameters, Vector &gradient );
161  static int numericalJacobian_static(const OptimizerSystem&,
162  const Vector& parameters, const bool new_parameters, Matrix& jacobian );
163 
164 protected:
165  // These methods are to be called by derived classes as an interface
166  // to the OptimizerSystem virtuals. The signature must match that required by
167  // IpOpt's matching callbacks. We're using the "user data" argument to pass in
168  // the current OptimizerRep, making these behave like non-static members.
169 
170  static int objectiveFuncWrapper ( int n, const Real* x, int new_x, Real* f, void* rep);
171  static int gradientFuncWrapper ( int n, const Real* x, int new_x, Real* gradient, void* rep);
172  static int constraintFuncWrapper( int n, const Real* x, int new_x, int m, Real* g, void* rep);
173  static int constraintJacobianWrapper( int n, const Real* x, int new_x,int m, int nele_jac,
174  int* iRow, int* jCol, Real* values, void* rep);
175  static int hessianWrapper( int n, const Real* x, int new_x, Real obj_factor,
176  int m, Real* lambda, int new_lambda,
177  int nele_hess, int* iRow, int* jCol,
178  Real* values, void* rep);
179 
188 
189 private:
190  const OptimizerSystem* sysp;
191  bool numericalGradient; // true if optimizer will compute an numerical gradient
192  bool numericalJacobian; // true if optimizer will compute an numerical Jacobian
193  Differentiator *gradDiff;
194  Differentiator *jacDiff;
195 
196  SysObjectiveFunc *of;
197  SysConstraintFunc *cf;
198 
199  std::map<std::string, std::string> advancedStrOptions;
200  std::map<std::string, Real> advancedRealOptions;
201  std::map<std::string, int> advancedIntOptions;
202  std::map<std::string, bool> advancedBoolOptions;
203 
204  friend class Optimizer;
205  Optimizer* myHandle; // The owner handle of this Rep.
206 
207 }; // end class OptimizerRep
208 
210  Real optimize( Vector &results );
211  OptimizerRep* clone() const;
212  OptimizerAlgorithm getAlgorithm() const;
213 };
214 
215 } // namespace SimTK
216 
217 
218 #endif // SimTK_SIMMATH_OPTIMIZER_REP_H_
Given a function f(y), where f, y or both can be vectors, calculate the derivative (gradient...
Definition: Differentiator.h:77
Real getEstimatedAccuracyOfConstraints() const
Definition: OptimizerRep.h:143
Definition: OptimizerRep.h:36
JacobianFunction(int nf=-1, int ny=-1, Real acc=-1)
virtual int objectiveFunc(const Vector &parameters, bool new_parameters, Real &f) const
Objective/cost function which is to be optimized; return 0 when successful.
Definition: Optimizer.h:98
void clearMyHandle()
Definition: OptimizerRep.h:132
GradientFunction(int ny=-1, Real acc=-1)
Real constraintsEstimatedAccuracy
Definition: OptimizerRep.h:187
Real constraintTolerance
Definition: OptimizerRep.h:182
API for SimTK Simmath's optimizers.
Definition: Optimizer.h:355
This is the top-level SimTK namespace into which all SimTK names are placed to avoid collision with o...
Definition: Assembler.h:37
int f(const Vector &y, Vector &fy) const
Definition: OptimizerRep.h:56
const Optimizer & getMyHandle() const
Definition: OptimizerRep.h:131
const OptimizerSystem * sysp
Definition: OptimizerRep.h:45
Definition: OptimizerRep.h:63
SimTK_Real Real
This is the default compiled-in floating point type for SimTK, either float or double.
Definition: SimTKcommon/include/SimTKcommon/internal/common.h:577
Real convergenceTolerance
Definition: OptimizerRep.h:181
void setMyHandle(Optimizer &cp)
Definition: OptimizerRep.h:130
const Differentiator & getJacobianDifferentiator() const
Definition: OptimizerRep.h:151
Real getEstimatedAccuracyOfObjective() const
Definition: OptimizerRep.h:141
SysObjectiveFunc(int ny, const OptimizerSystem *sysPtr)
Definition: OptimizerRep.h:38
static bool isAvailable()
Definition: OptimizerRep.h:107
int maxIterations
Definition: OptimizerRep.h:183
Includes internal headers providing declarations for the basic SimTK Core classes, including Simmatrix.
int diagnosticsLevel
Definition: OptimizerRep.h:180
virtual OptimizerRep * clone() const
Definition: OptimizerRep.h:106
Real objectiveEstimatedAccuracy
Definition: OptimizerRep.h:186
OptimizerRep(const OptimizerSystem &sys)
Definition: OptimizerRep.h:66
int f(const Vector &y, Real &fy) const
Definition: OptimizerRep.h:42
Method
Definition: Differentiator.h:92
Definition: OptimizerRep.h:50
bool isUsingNumericalGradient() const
Definition: OptimizerRep.h:138
const OptimizerSystem * sysp
Definition: OptimizerRep.h:59
SysConstraintFunc(int nf, int ny, const OptimizerSystem *sysPtr)
Definition: OptimizerRep.h:52
int limitedMemoryHistory
Definition: OptimizerRep.h:184
OptimizerRep()
Definition: OptimizerRep.h:86
virtual int constraintFunc(const Vector &parameters, bool new_parameters, Vector &constraints) const
Computes the value of the constraints; return 0 when successful.
Definition: Optimizer.h:111
This is the header file that user code should include to pick up the SimTK Simmath numerical differen...
const Real SignificantReal
SignificantReal is the smallest value that we consider to be clearly distinct from roundoff error whe...
const Differentiator & getGradientDifferentiator() const
Definition: OptimizerRep.h:146
Abstract class which defines an objective/cost function which is optimized by and Optimizer object...
Definition: Optimizer.h:71
bool isUsingNumericalJacobian() const
Definition: OptimizerRep.h:139
const OptimizerSystem & getOptimizerSystem() const
Definition: OptimizerRep.h:111
Differentiator::Method getDifferentiatorMethod() const
Definition: OptimizerRep.h:140
Derive a concrete class from this one if you have a scalar function of multiple variables that you wa...
Definition: Differentiator.h:208
Definition: Optimizer.h:61
OptimizerAlgorithm
The available Optimizer algorithms.
Definition: Optimizer.h:40
#define SimTK_SIMMATH_EXPORT
Definition: SimTKmath/include/simmath/internal/common.h:64
Derive a concrete class from this one if you have a set of functions (i.e., a vector-valued function)...
Definition: Differentiator.h:227
Definition: OptimizerRep.h:209
virtual OptimizerAlgorithm getAlgorithm() const
Definition: OptimizerRep.h:156
Differentiator::Method diffMethod
Definition: OptimizerRep.h:185