/* Include "petsctao.h" so that we can use TAO solvers. Note that this file automatically includes libraries such as: petsc.h - base PETSc routines petscvec.h - vectors petscsys.h - system routines petscmat.h - matrices petscis.h - index sets petscksp.h - Krylov subspace methods petscviewer.h - viewers petscpc.h - preconditioners This version tests correlated terms using both vector and listed forms */ #include /* Description: These data are the result of a NIST study involving ultrasonic calibration. The response variable is ultrasonic response, and the predictor variable is metal distance. Reference: Chwirut, D., NIST (197?). Ultrasonic Reference Block Study. */ static char help[] = "Finds the nonlinear least-squares solution to the model \n\ y = exp[-b1*x]/(b2+b3*x) + e \n"; #define NOBSERVATIONS 214 #define NPARAMETERS 3 /* User-defined application context */ typedef struct { /* Working space */ PetscReal t[NOBSERVATIONS]; /* array of independent variables of observation */ PetscReal y[NOBSERVATIONS]; /* array of dependent variables */ PetscReal j[NOBSERVATIONS][NPARAMETERS]; /* dense jacobian matrix array*/ PetscInt idm[NOBSERVATIONS]; /* Matrix indices for jacobian */ PetscInt idn[NPARAMETERS]; } AppCtx; /* User provided Routines */ PetscErrorCode InitializeData(AppCtx *user); PetscErrorCode FormStartingPoint(Vec); PetscErrorCode EvaluateFunction(Tao, Vec, Vec, void *); PetscErrorCode EvaluateJacobian(Tao, Vec, Mat, Mat, void *); /*--------------------------------------------------------------------*/ int main(int argc, char **argv) { PetscInt wtype = 0; Vec x, f; /* solution, function */ Vec w; /* weights */ Mat J; /* Jacobian matrix */ Tao tao; /* Tao solver context */ PetscInt i; /* iteration information */ PetscReal hist[100], resid[100]; PetscInt lits[100]; PetscInt w_row[NOBSERVATIONS]; /* explicit weights */ PetscInt w_col[NOBSERVATIONS]; PetscReal w_vals[NOBSERVATIONS]; PetscBool flg; AppCtx user; /* user-defined work context */ PetscFunctionBeginUser; PetscCall(PetscInitialize(&argc, &argv, NULL, help)); PetscCall(PetscOptionsGetInt(NULL, NULL, "-wtype", &wtype, &flg)); PetscCall(PetscPrintf(PETSC_COMM_WORLD, "wtype=%" PetscInt_FMT "\n", wtype)); /* Allocate vectors */ PetscCall(VecCreateSeq(MPI_COMM_SELF, NPARAMETERS, &x)); PetscCall(VecCreateSeq(MPI_COMM_SELF, NOBSERVATIONS, &f)); PetscCall(VecDuplicate(f, &w)); /* no correlation, but set in different ways */ PetscCall(VecSet(w, 1.0)); for (i = 0; i < NOBSERVATIONS; i++) { w_row[i] = i; w_col[i] = i; w_vals[i] = 1.0; } /* Create the Jacobian matrix. */ PetscCall(MatCreateSeqDense(MPI_COMM_SELF, NOBSERVATIONS, NPARAMETERS, NULL, &J)); for (i = 0; i < NOBSERVATIONS; i++) user.idm[i] = i; for (i = 0; i < NPARAMETERS; i++) user.idn[i] = i; /* Create TAO solver and set desired solution method */ PetscCall(TaoCreate(PETSC_COMM_SELF, &tao)); PetscCall(TaoSetType(tao, TAOPOUNDERS)); /* Set the function and Jacobian routines. */ PetscCall(InitializeData(&user)); PetscCall(FormStartingPoint(x)); PetscCall(TaoSetSolution(tao, x)); PetscCall(TaoSetResidualRoutine(tao, f, EvaluateFunction, (void *)&user)); if (wtype == 1) { PetscCall(TaoSetResidualWeights(tao, w, 0, NULL, NULL, NULL)); } else if (wtype == 2) { PetscCall(TaoSetResidualWeights(tao, NULL, NOBSERVATIONS, w_row, w_col, w_vals)); } PetscCall(TaoSetJacobianResidualRoutine(tao, J, J, EvaluateJacobian, (void *)&user)); PetscCall(TaoSetTolerances(tao, 1e-5, 0.0, PETSC_CURRENT)); /* Check for any TAO command line arguments */ PetscCall(TaoSetFromOptions(tao)); PetscCall(TaoSetConvergenceHistory(tao, hist, resid, 0, lits, 100, PETSC_TRUE)); /* Perform the Solve */ PetscCall(TaoSolve(tao)); /* Free TAO data structures */ PetscCall(TaoDestroy(&tao)); /* Free PETSc data structures */ PetscCall(VecDestroy(&x)); PetscCall(VecDestroy(&w)); PetscCall(VecDestroy(&f)); PetscCall(MatDestroy(&J)); PetscCall(PetscFinalize()); return 0; } /*--------------------------------------------------------------------*/ PetscErrorCode EvaluateFunction(Tao tao, Vec X, Vec F, void *ptr) { AppCtx *user = (AppCtx *)ptr; PetscInt i; PetscReal *y = user->y, *f, *t = user->t; const PetscReal *x; PetscFunctionBegin; PetscCall(VecGetArrayRead(X, &x)); PetscCall(VecGetArray(F, &f)); for (i = 0; i < NOBSERVATIONS; i++) f[i] = y[i] - PetscExpScalar(-x[0] * t[i]) / (x[1] + x[2] * t[i]); PetscCall(VecRestoreArrayRead(X, &x)); PetscCall(VecRestoreArray(F, &f)); PetscCall(PetscLogFlops(6 * NOBSERVATIONS)); PetscFunctionReturn(PETSC_SUCCESS); } /*------------------------------------------------------------*/ /* J[i][j] = df[i]/dt[j] */ PetscErrorCode EvaluateJacobian(Tao tao, Vec X, Mat J, Mat Jpre, void *ptr) { AppCtx *user = (AppCtx *)ptr; PetscInt i; PetscReal *t = user->t; const PetscReal *x; PetscReal base; PetscFunctionBegin; PetscCall(VecGetArrayRead(X, &x)); for (i = 0; i < NOBSERVATIONS; i++) { base = PetscExpScalar(-x[0] * t[i]) / (x[1] + x[2] * t[i]); user->j[i][0] = t[i] * base; user->j[i][1] = base / (x[1] + x[2] * t[i]); user->j[i][2] = base * t[i] / (x[1] + x[2] * t[i]); } /* Assemble the matrix */ PetscCall(MatSetValues(J, NOBSERVATIONS, user->idm, NPARAMETERS, user->idn, (PetscReal *)user->j, INSERT_VALUES)); PetscCall(MatAssemblyBegin(J, MAT_FINAL_ASSEMBLY)); PetscCall(MatAssemblyEnd(J, MAT_FINAL_ASSEMBLY)); PetscCall(VecRestoreArrayRead(X, &x)); PetscCall(PetscLogFlops(NOBSERVATIONS * 13)); PetscFunctionReturn(PETSC_SUCCESS); } /* ------------------------------------------------------------ */ PetscErrorCode FormStartingPoint(Vec X) { PetscReal *x; PetscFunctionBegin; PetscCall(VecGetArray(X, &x)); x[0] = 1.19; x[1] = -1.86; x[2] = 1.08; PetscCall(VecRestoreArray(X, &x)); PetscFunctionReturn(PETSC_SUCCESS); } /* ---------------------------------------------------------------------- */ PetscErrorCode InitializeData(AppCtx *user) { PetscReal *t = user->t, *y = user->y; PetscInt i = 0; PetscFunctionBegin; y[i] = 92.9000; t[i++] = 0.5000; y[i] = 78.7000; t[i++] = 0.6250; y[i] = 64.2000; t[i++] = 0.7500; y[i] = 64.9000; t[i++] = 0.8750; y[i] = 57.1000; t[i++] = 1.0000; y[i] = 43.3000; t[i++] = 1.2500; y[i] = 31.1000; t[i++] = 1.7500; y[i] = 23.6000; t[i++] = 2.2500; y[i] = 31.0500; t[i++] = 1.7500; y[i] = 23.7750; t[i++] = 2.2500; y[i] = 17.7375; t[i++] = 2.7500; y[i] = 13.8000; t[i++] = 3.2500; y[i] = 11.5875; t[i++] = 3.7500; y[i] = 9.4125; t[i++] = 4.2500; y[i] = 7.7250; t[i++] = 4.7500; y[i] = 7.3500; t[i++] = 5.2500; y[i] = 8.0250; t[i++] = 5.7500; y[i] = 90.6000; t[i++] = 0.5000; y[i] = 76.9000; t[i++] = 0.6250; y[i] = 71.6000; t[i++] = 0.7500; y[i] = 63.6000; t[i++] = 0.8750; y[i] = 54.0000; t[i++] = 1.0000; y[i] = 39.2000; t[i++] = 1.2500; y[i] = 29.3000; t[i++] = 1.7500; y[i] = 21.4000; t[i++] = 2.2500; y[i] = 29.1750; t[i++] = 1.7500; y[i] = 22.1250; t[i++] = 2.2500; y[i] = 17.5125; t[i++] = 2.7500; y[i] = 14.2500; t[i++] = 3.2500; y[i] = 9.4500; t[i++] = 3.7500; y[i] = 9.1500; t[i++] = 4.2500; y[i] = 7.9125; t[i++] = 4.7500; y[i] = 8.4750; t[i++] = 5.2500; y[i] = 6.1125; t[i++] = 5.7500; y[i] = 80.0000; t[i++] = 0.5000; y[i] = 79.0000; t[i++] = 0.6250; y[i] = 63.8000; t[i++] = 0.7500; y[i] = 57.2000; t[i++] = 0.8750; y[i] = 53.2000; t[i++] = 1.0000; y[i] = 42.5000; t[i++] = 1.2500; y[i] = 26.8000; t[i++] = 1.7500; y[i] = 20.4000; t[i++] = 2.2500; y[i] = 26.8500; t[i++] = 1.7500; y[i] = 21.0000; t[i++] = 2.2500; y[i] = 16.4625; t[i++] = 2.7500; y[i] = 12.5250; t[i++] = 3.2500; y[i] = 10.5375; t[i++] = 3.7500; y[i] = 8.5875; t[i++] = 4.2500; y[i] = 7.1250; t[i++] = 4.7500; y[i] = 6.1125; t[i++] = 5.2500; y[i] = 5.9625; t[i++] = 5.7500; y[i] = 74.1000; t[i++] = 0.5000; y[i] = 67.3000; t[i++] = 0.6250; y[i] = 60.8000; t[i++] = 0.7500; y[i] = 55.5000; t[i++] = 0.8750; y[i] = 50.3000; t[i++] = 1.0000; y[i] = 41.0000; t[i++] = 1.2500; y[i] = 29.4000; t[i++] = 1.7500; y[i] = 20.4000; t[i++] = 2.2500; y[i] = 29.3625; t[i++] = 1.7500; y[i] = 21.1500; t[i++] = 2.2500; y[i] = 16.7625; t[i++] = 2.7500; y[i] = 13.2000; t[i++] = 3.2500; y[i] = 10.8750; t[i++] = 3.7500; y[i] = 8.1750; t[i++] = 4.2500; y[i] = 7.3500; t[i++] = 4.7500; y[i] = 5.9625; t[i++] = 5.2500; y[i] = 5.6250; t[i++] = 5.7500; y[i] = 81.5000; t[i++] = .5000; y[i] = 62.4000; t[i++] = .7500; y[i] = 32.5000; t[i++] = 1.5000; y[i] = 12.4100; t[i++] = 3.0000; y[i] = 13.1200; t[i++] = 3.0000; y[i] = 15.5600; t[i++] = 3.0000; y[i] = 5.6300; t[i++] = 6.0000; y[i] = 78.0000; t[i++] = .5000; y[i] = 59.9000; t[i++] = .7500; y[i] = 33.2000; t[i++] = 1.5000; y[i] = 13.8400; t[i++] = 3.0000; y[i] = 12.7500; t[i++] = 3.0000; y[i] = 14.6200; t[i++] = 3.0000; y[i] = 3.9400; t[i++] = 6.0000; y[i] = 76.8000; t[i++] = .5000; y[i] = 61.0000; t[i++] = .7500; y[i] = 32.9000; t[i++] = 1.5000; y[i] = 13.8700; t[i++] = 3.0000; y[i] = 11.8100; t[i++] = 3.0000; y[i] = 13.3100; t[i++] = 3.0000; y[i] = 5.4400; t[i++] = 6.0000; y[i] = 78.0000; t[i++] = .5000; y[i] = 63.5000; t[i++] = .7500; y[i] = 33.8000; t[i++] = 1.5000; y[i] = 12.5600; t[i++] = 3.0000; y[i] = 5.6300; t[i++] = 6.0000; y[i] = 12.7500; t[i++] = 3.0000; y[i] = 13.1200; t[i++] = 3.0000; y[i] = 5.4400; t[i++] = 6.0000; y[i] = 76.8000; t[i++] = .5000; y[i] = 60.0000; t[i++] = .7500; y[i] = 47.8000; t[i++] = 1.0000; y[i] = 32.0000; t[i++] = 1.5000; y[i] = 22.2000; t[i++] = 2.0000; y[i] = 22.5700; t[i++] = 2.0000; y[i] = 18.8200; t[i++] = 2.5000; y[i] = 13.9500; t[i++] = 3.0000; y[i] = 11.2500; t[i++] = 4.0000; y[i] = 9.0000; t[i++] = 5.0000; y[i] = 6.6700; t[i++] = 6.0000; y[i] = 75.8000; t[i++] = .5000; y[i] = 62.0000; t[i++] = .7500; y[i] = 48.8000; t[i++] = 1.0000; y[i] = 35.2000; t[i++] = 1.5000; y[i] = 20.0000; t[i++] = 2.0000; y[i] = 20.3200; t[i++] = 2.0000; y[i] = 19.3100; t[i++] = 2.5000; y[i] = 12.7500; t[i++] = 3.0000; y[i] = 10.4200; t[i++] = 4.0000; y[i] = 7.3100; t[i++] = 5.0000; y[i] = 7.4200; t[i++] = 6.0000; y[i] = 70.5000; t[i++] = .5000; y[i] = 59.5000; t[i++] = .7500; y[i] = 48.5000; t[i++] = 1.0000; y[i] = 35.8000; t[i++] = 1.5000; y[i] = 21.0000; t[i++] = 2.0000; y[i] = 21.6700; t[i++] = 2.0000; y[i] = 21.0000; t[i++] = 2.5000; y[i] = 15.6400; t[i++] = 3.0000; y[i] = 8.1700; t[i++] = 4.0000; y[i] = 8.5500; t[i++] = 5.0000; y[i] = 10.1200; t[i++] = 6.0000; y[i] = 78.0000; t[i++] = .5000; y[i] = 66.0000; t[i++] = .6250; y[i] = 62.0000; t[i++] = .7500; y[i] = 58.0000; t[i++] = .8750; y[i] = 47.7000; t[i++] = 1.0000; y[i] = 37.8000; t[i++] = 1.2500; y[i] = 20.2000; t[i++] = 2.2500; y[i] = 21.0700; t[i++] = 2.2500; y[i] = 13.8700; t[i++] = 2.7500; y[i] = 9.6700; t[i++] = 3.2500; y[i] = 7.7600; t[i++] = 3.7500; y[i] = 5.4400; t[i++] = 4.2500; y[i] = 4.8700; t[i++] = 4.7500; y[i] = 4.0100; t[i++] = 5.2500; y[i] = 3.7500; t[i++] = 5.7500; y[i] = 24.1900; t[i++] = 3.0000; y[i] = 25.7600; t[i++] = 3.0000; y[i] = 18.0700; t[i++] = 3.0000; y[i] = 11.8100; t[i++] = 3.0000; y[i] = 12.0700; t[i++] = 3.0000; y[i] = 16.1200; t[i++] = 3.0000; y[i] = 70.8000; t[i++] = .5000; y[i] = 54.7000; t[i++] = .7500; y[i] = 48.0000; t[i++] = 1.0000; y[i] = 39.8000; t[i++] = 1.5000; y[i] = 29.8000; t[i++] = 2.0000; y[i] = 23.7000; t[i++] = 2.5000; y[i] = 29.6200; t[i++] = 2.0000; y[i] = 23.8100; t[i++] = 2.5000; y[i] = 17.7000; t[i++] = 3.0000; y[i] = 11.5500; t[i++] = 4.0000; y[i] = 12.0700; t[i++] = 5.0000; y[i] = 8.7400; t[i++] = 6.0000; y[i] = 80.7000; t[i++] = .5000; y[i] = 61.3000; t[i++] = .7500; y[i] = 47.5000; t[i++] = 1.0000; y[i] = 29.0000; t[i++] = 1.5000; y[i] = 24.0000; t[i++] = 2.0000; y[i] = 17.7000; t[i++] = 2.5000; y[i] = 24.5600; t[i++] = 2.0000; y[i] = 18.6700; t[i++] = 2.5000; y[i] = 16.2400; t[i++] = 3.0000; y[i] = 8.7400; t[i++] = 4.0000; y[i] = 7.8700; t[i++] = 5.0000; y[i] = 8.5100; t[i++] = 6.0000; y[i] = 66.7000; t[i++] = .5000; y[i] = 59.2000; t[i++] = .7500; y[i] = 40.8000; t[i++] = 1.0000; y[i] = 30.7000; t[i++] = 1.5000; y[i] = 25.7000; t[i++] = 2.0000; y[i] = 16.3000; t[i++] = 2.5000; y[i] = 25.9900; t[i++] = 2.0000; y[i] = 16.9500; t[i++] = 2.5000; y[i] = 13.3500; t[i++] = 3.0000; y[i] = 8.6200; t[i++] = 4.0000; y[i] = 7.2000; t[i++] = 5.0000; y[i] = 6.6400; t[i++] = 6.0000; y[i] = 13.6900; t[i++] = 3.0000; y[i] = 81.0000; t[i++] = .5000; y[i] = 64.5000; t[i++] = .7500; y[i] = 35.5000; t[i++] = 1.5000; y[i] = 13.3100; t[i++] = 3.0000; y[i] = 4.8700; t[i++] = 6.0000; y[i] = 12.9400; t[i++] = 3.0000; y[i] = 5.0600; t[i++] = 6.0000; y[i] = 15.1900; t[i++] = 3.0000; y[i] = 14.6200; t[i++] = 3.0000; y[i] = 15.6400; t[i++] = 3.0000; y[i] = 25.5000; t[i++] = 1.7500; y[i] = 25.9500; t[i++] = 1.7500; y[i] = 81.7000; t[i++] = .5000; y[i] = 61.6000; t[i++] = .7500; y[i] = 29.8000; t[i++] = 1.7500; y[i] = 29.8100; t[i++] = 1.7500; y[i] = 17.1700; t[i++] = 2.7500; y[i] = 10.3900; t[i++] = 3.7500; y[i] = 28.4000; t[i++] = 1.7500; y[i] = 28.6900; t[i++] = 1.7500; y[i] = 81.3000; t[i++] = .5000; y[i] = 60.9000; t[i++] = .7500; y[i] = 16.6500; t[i++] = 2.7500; y[i] = 10.0500; t[i++] = 3.7500; y[i] = 28.9000; t[i++] = 1.7500; y[i] = 28.9500; t[i++] = 1.7500; PetscFunctionReturn(PETSC_SUCCESS); } /*TEST build: requires: !complex test: args: -tao_monitor_short -tao_max_it 100 -tao_type pounders -tao_pounders_delta 0.05 -tao_gatol 1.e-5 requires: !single TODO: produces different output for many different systems test: suffix: 2 args: -tao_monitor_short -tao_max_it 100 -wtype 1 -tao_type pounders -tao_pounders_delta 0.05 -tao_gatol 1.e-5 requires: !single TODO: produces different output for many different systems test: suffix: 3 args: -tao_monitor_short -tao_max_it 100 -wtype 2 -tao_type pounders -tao_pounders_delta 0.05 -tao_gatol 1.e-5 requires: !single TODO: produces different output for many different systems test: suffix: 4 args: -tao_monitor_short -tao_max_it 100 -tao_type pounders -tao_pounders_delta 0.05 -pounders_subsolver_tao_type blmvm -tao_gatol 1.e-5 requires: !single TODO: produces different output for many different systems TEST*/