split every algorithm in *Init() + while(running) { *OneStep() }

unsupported/Eigen/src/NonLinear/HybridNonLinearSolver.h

@@ -37,6 +37,17 @@ public:
             Matrix< Scalar, Dynamic, 1 >  &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
+
+    Status solveInit(
+            Matrix< Scalar, Dynamic, 1 >  &x,
+            const Parameters &parameters,
+            const int mode=1
+            );
+    Status solveOneStep(
+            Matrix< Scalar, Dynamic, 1 >  &x,
+            const Parameters &parameters,
+            const int mode=1
+            );
     Status solve(
             Matrix< Scalar, Dynamic, 1 >  &x,
             const Parameters &parameters,
@@ -47,6 +58,17 @@ public:
             Matrix< Scalar, Dynamic, 1 >  &x,
             const Scalar tol = ei_sqrt(epsilon<Scalar>())
             );
+
+    Status solveNumericalDiffInit(
+            Matrix< Scalar, Dynamic, 1 >  &x,
+            const Parameters &parameters,
+            const int mode=1
+            );
+    Status solveNumericalDiffOneStep(
+            Matrix< Scalar, Dynamic, 1 >  &x,
+            const Parameters &parameters,
+            const int mode=1
+            );
     Status solveNumericalDiff(
             Matrix< Scalar, Dynamic, 1 >  &x,
             const Parameters &parameters,
@@ -107,11 +129,9 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
     );
 }
 
-
-
 template<typename FunctorType, typename Scalar>
 typename HybridNonLinearSolver<FunctorType,Scalar>::Status
-HybridNonLinearSolver<FunctorType,Scalar>::solve(
+HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
         Matrix< Scalar, Dynamic, 1 >  &x,
         const Parameters &parameters,
         const int mode
@@ -124,7 +144,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
     qtf.resize(n);
     R.resize( (n*(n+1))/2);
     fjac.resize(n, n);
-    fvec.resize(n);
     if (mode != 2)
         diag.resize(n);
     assert( (mode!=2 || diag.size()==n) || "When using mode==2, the caller must provide a valid 'diag'");
@@ -158,9 +177,17 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
     nslow1 = 0;
     nslow2 = 0;
 
-    /*     beginning of the outer loop. */
+    return Running;
+}
 
-    while (true) {
+template<typename FunctorType, typename Scalar>
+typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        const Parameters &parameters,
+        const int mode
+        )
+{
     int i, j, l, iwa[1];
     jeval = true;
 
@@ -370,8 +397,23 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
         jeval = false;
     }
     /* end of the outer loop. */
+
+    return Running;
 }
-    assert(false); // should never be reached
+
+
+template<typename FunctorType, typename Scalar>
+typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolver<FunctorType,Scalar>::solve(
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        const Parameters &parameters,
+        const int mode
+        )
+{
+    Status status = solveInit(x, parameters, mode);
+    while (status==Running)
+        status = solveOneStep(x, parameters, mode);
+    return status;
 }
 
 
@@ -403,10 +445,9 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
     );
 }
 
-
 template<typename FunctorType, typename Scalar>
 typename HybridNonLinearSolver<FunctorType,Scalar>::Status
-HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
+HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
         Matrix< Scalar, Dynamic, 1 >  &x,
         const Parameters &parameters,
         const int mode
@@ -428,6 +469,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
         diag.resize(n);
     assert( (mode!=2 || diag.size()==n) || "When using mode==2, the caller must provide a valid 'diag'");
 
+
     /* Function Body */
 
     nfev = 0;
@@ -457,11 +499,23 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
     nslow1 = 0;
     nslow2 = 0;
 
-    /*     beginning of the outer loop. */
+    return Running;
+}
 
-    while (true) {
+template<typename FunctorType, typename Scalar>
+typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        const Parameters &parameters,
+        const int mode
+        )
+{
     int i, j, l, iwa[1];
     jeval = true;
+    int nsub = parameters.nb_of_subdiagonals;
+    int nsup = parameters.nb_of_superdiagonals;
+    if (nsub<0) nsub= n-1;
+    if (nsup<0) nsup= n-1;
 
     /* calculate the jacobian matrix. */
 
@@ -670,7 +724,21 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
         jeval = false;
     }
     /* end of the outer loop. */
-    }
+
-    assert(false); // should never be reached
+    return Running;
+}
+
+template<typename FunctorType, typename Scalar>
+typename HybridNonLinearSolver<FunctorType,Scalar>::Status
+HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        const Parameters &parameters,
+        const int mode
+        )
+{
+    Status status = solveNumericalDiffInit(x, parameters, mode);
+    while (status==Running)
+        status = solveNumericalDiffOneStep(x, parameters, mode);
+    return status;
 }
 

unsupported/Eigen/src/NonLinear/LevenbergMarquardt.h

@@ -46,6 +46,16 @@ public:
             const Parameters &parameters,
             const int mode=1
             );
+    Status minimizeInit(
+            Matrix< Scalar, Dynamic, 1 >  &x,
+            const Parameters &parameters,
+            const int mode=1
+            );
+    Status minimizeOneStep(
+            Matrix< Scalar, Dynamic, 1 >  &x,
+            const Parameters &parameters,
+            const int mode=1
+            );
 
     Status minimizeNumericalDiff(
             Matrix< Scalar, Dynamic, 1 >  &x,
@@ -57,6 +67,16 @@ public:
             const Parameters &parameters,
             const int mode=1
             );
+    Status minimizeNumericalDiffInit(
+            Matrix< Scalar, Dynamic, 1 >  &x,
+            const Parameters &parameters,
+            const int mode=1
+            );
+    Status minimizeNumericalDiffOneStep(
+            Matrix< Scalar, Dynamic, 1 >  &x,
+            const Parameters &parameters,
+            const int mode=1
+            );
 
     Status minimizeOptimumStorage(
             Matrix< Scalar, Dynamic, 1 >  &x,
@@ -68,6 +88,16 @@ public:
             const Parameters &parameters,
             const int mode=1
             );
+    Status minimizeOptimumStorageInit(
+            Matrix< Scalar, Dynamic, 1 >  &x,
+            const Parameters &parameters,
+            const int mode=1
+            );
+    Status minimizeOptimumStorageOneStep(
+            Matrix< Scalar, Dynamic, 1 >  &x,
+            const Parameters &parameters,
+            const int mode=1
+            );
 
     Matrix< Scalar, Dynamic, 1 >  fvec;
     Matrix< Scalar, Dynamic, Dynamic > fjac;
@@ -126,6 +156,20 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
         const Parameters &parameters,
         const int mode
         )
+{
+    Status status = minimizeInit(x, parameters, mode);
+    while (status==Running)
+        status = minimizeOneStep(x, parameters, mode);
+    return status;
+}
+
+template<typename FunctorType, typename Scalar>
+typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        const Parameters &parameters,
+        const int mode
+        )
 {
     n = x.size();
     m = functor.nbOfFunctions();
@@ -167,9 +211,17 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
     par = 0.;
     iter = 1;
 
-    /*     beginning of the outer loop. */
+    return Running;
+}
 
-    while (true) {
+template<typename FunctorType, typename Scalar>
+typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        const Parameters &parameters,
+        const int mode
+        )
+{
     int i, j, l;
 
     /* calculate the jacobian matrix. */
@@ -352,9 +404,9 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
         /* end of the inner loop. repeat if iteration unsuccessful. */
     } while (ratio < Scalar(1e-4));
     /* end of the outer loop. */
-    }
-}
 
+    return Running;
+}
 
 template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
@@ -385,7 +437,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
 
 template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
+LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiffInit(
         Matrix< Scalar, Dynamic, 1 >  &x,
         const Parameters &parameters,
         const int mode
@@ -429,9 +481,17 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
     par = 0.;
     iter = 1;
 
-    /*     beginning of the outer loop. */
+    return Running;
+}
 
-    while (true) {
+template<typename FunctorType, typename Scalar>
+typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiffOneStep(
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        const Parameters &parameters,
+        const int mode
+        )
+{
     int i, j, l;
 
     /* calculate the jacobian matrix. */
@@ -615,8 +675,22 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
         /* end of the inner loop. repeat if iteration unsuccessful. */
     } while (ratio < Scalar(1e-4));
     /* end of the outer loop. */
+    return Running;
 }
-    assert(false); // should never be reached
+
+
+template<typename FunctorType, typename Scalar>
+typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        const Parameters &parameters,
+        const int mode
+        )
+{
+    Status status = minimizeNumericalDiffInit(x, parameters, mode);
+    while (status==Running)
+        status = minimizeNumericalDiffOneStep(x, parameters, mode);
+    return status;
 }
 
 
@@ -651,7 +725,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
 
 template<typename FunctorType, typename Scalar>
 typename LevenbergMarquardt<FunctorType,Scalar>::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
+LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
         Matrix< Scalar, Dynamic, 1 >  &x,
         const Parameters &parameters,
         const int mode
@@ -670,9 +744,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
     assert( (mode!=2 || diag.size()==n) || "When using mode==2, the caller must provide a valid 'diag'");
     qtf.resize(n);
 
-    /* Local variables */
-    bool sing;
-
     /* Function Body */
     nfev = 0;
     njev = 0;
@@ -700,10 +771,20 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
     par = 0.;
     iter = 1;
 
-    /*     beginning of the outer loop. */
+    return Running;
+}
 
-    while (true) {
+
+template<typename FunctorType, typename Scalar>
+typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        const Parameters &parameters,
+        const int mode
+        )
+{
     int i, j, l;
+    bool sing;
 
     /* compute the qr factorization of the jacobian matrix */
     /* calculated one row at a time, while simultaneously */
@@ -902,8 +983,23 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
         /* end of the inner loop. repeat if iteration unsuccessful. */
     } while (ratio < Scalar(1e-4));
     /* end of the outer loop. */
-    }
+
-    assert(false); // should never be reached
+    return Running;
+}
+
+
+template<typename FunctorType, typename Scalar>
+typename LevenbergMarquardt<FunctorType,Scalar>::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        const Parameters &parameters,
+        const int mode
+        )
+{
+    Status status = minimizeOptimumStorageInit(x, parameters, mode);
+    while (status==Running)
+        status = minimizeOptimumStorageOneStep(x, parameters, mode);
+    return status;
 }
 
 

unsupported/test/NonLinear.cpp

@@ -1836,6 +1836,7 @@ void test_NonLinear()
   printf("x[1] : %.32g\n", x[1]);
   printf("x[2] : %.32g\n", x[2]);
   printf("x[3] : %.32g\n", x[3]);
-  printf("fvec.squaredNorm() : %.32g\n", fvec.squaredNorm());
+  printf("fvec.blueNorm() : %.32g\n", solver.fvec.blueNorm());
+  printf("fvec.blueNorm() : %.32g\n", lm.fvec.blueNorm());
 */