/*
 * -----------------------------------------------------------------
 * $Revision: 1.5 $
 * $Date: 2007/07/27 23:52:53 $
 * ----------------------------------------------------------------- 
 * Programmers: Radu Serban @ LLNL
 * -----------------------------------------------------------------
 * Copyright (c) 2002, The Regents of the University of California  
 * Produced at the Lawrence Livermore National Laboratory
 * All rights reserved
 * For details, see the LICENSE file
 * -----------------------------------------------------------------
 * This is the implementation file for the IC calculation for IDAS.
 * It is independent of the linear solver in use.                  
 * -----------------------------------------------------------------
 */

#include <stdio.h>
#include <stdlib.h>

#include "idas_impl.h"
#include <sundials/sundials_math.h>

/* Macro: loop */
#define loop for(;;)

/*
 * =================================================================
 * IDA Constants 
 * =================================================================
 */

/* Private Constants */

#define ZERO       RCONST(0.0)    /* real 0.0    */
#define HALF       RCONST(0.5)    /* real 0.5    */
#define ONE        RCONST(1.0)    /* real 1.0    */
#define TWO        RCONST(2.0)    /* real 2.0    */
#define PT99       RCONST(0.99)   /* real 0.99   */
#define PT1        RCONST(0.1)    /* real 0.1    */
#define PT001      RCONST(0.001)  /* real 0.001  */

/* IDACalcIC control constants */

#define ICRATEMAX  RCONST(0.9)    /* max. Newton conv. rate */
#define ALPHALS    RCONST(0.0001) /* alpha in linesearch conv. test */

/* Return values for lower level routines used by IDACalcIC */

#define IC_FAIL_RECOV       1
#define IC_CONSTR_FAILED    2
#define IC_LINESRCH_FAILED  3
#define IC_CONV_FAIL        4  
#define IC_SLOW_CONVRG      5

/*
 * =================================================================
 * Private Helper Functions Prototypes
 * =================================================================
 */

extern int IDAInitialSetup(IDAMem IDA_mem);
extern realtype IDAWrmsNorm(IDAMem IDA_mem, N_Vector x, 
                            N_Vector w, booleantype mask);
extern realtype IDASensWrmsNorm(IDAMem IDA_mem, N_Vector *xS, 
                                N_Vector *wS, booleantype mask);
extern realtype IDASensWrmsNormUpdate(IDAMem IDA_mem, realtype old_nrm,
                                      N_Vector *xS, N_Vector *wS,
                                      booleantype mask);

extern int IDASensEwtSet(IDAMem IDA_mem, N_Vector *yScur, N_Vector *weightS);

static int IDANlsIC(IDAMem IDA_mem);

static int IDANewtonIC(IDAMem IDA_mem);
static int IDALineSrch(IDAMem IDA_mem, realtype *delnorm, realtype *fnorm);
static int IDAfnorm(IDAMem IDA_mem, realtype *fnorm);
static int IDANewyyp(IDAMem IDA_mem, realtype lambda);
static int IDANewy(IDAMem IDA_mem);

static int IDASensNewtonIC(IDAMem IDA_mem);
static int IDASensLineSrch(IDAMem IDA_mem, realtype *delnorm, realtype *fnorm);
static int IDASensNewyyp(IDAMem IDA_mem, realtype lambda);
static int IDASensfnorm(IDAMem IDA_mem, realtype *fnorm);
static int IDASensNlsIC(IDAMem IDA_mem);

static int IDAICFailFlag(IDAMem IDA_mem, int retval);

/*
 * =================================================================
 * Readibility Constants
 * =================================================================
 */

#define t0             (IDA_mem->ida_t0)
#define yy0            (IDA_mem->ida_yy0)
#define yp0            (IDA_mem->ida_yp0)

#define user_data      (IDA_mem->ida_user_data)
#define res            (IDA_mem->ida_res)
#define efun           (IDA_mem->ida_efun)
#define edata          (IDA_mem->ida_edata)
#define uround         (IDA_mem->ida_uround)  
#define phi            (IDA_mem->ida_phi) 
#define ewt            (IDA_mem->ida_ewt)  
#define delta          (IDA_mem->ida_delta)
#define ee             (IDA_mem->ida_ee)
#define savres         (IDA_mem->ida_savres)
#define tempv2         (IDA_mem->ida_tempv2) 
#define hh             (IDA_mem->ida_hh)
#define tn             (IDA_mem->ida_tn)
#define cj             (IDA_mem->ida_cj)
#define cjratio        (IDA_mem->ida_cjratio)
#define nbacktr        (IDA_mem->ida_nbacktr)
#define nre            (IDA_mem->ida_nre)
#define ncfn           (IDA_mem->ida_ncfn)
#define nni            (IDA_mem->ida_nni)
#define nsetups        (IDA_mem->ida_nsetups)
#define ns             (IDA_mem->ida_ns)
#define lsetup         (IDA_mem->ida_lsetup)
#define lsolve         (IDA_mem->ida_lsolve) 
#define hused          (IDA_mem->ida_hused)         
#define epsNewt        (IDA_mem->ida_epsNewt)
#define id             (IDA_mem->ida_id)
#define setupNonNull   (IDA_mem->ida_setupNonNull) 
#define suppressalg    (IDA_mem->ida_suppressalg)
#define constraints    (IDA_mem->ida_constraints)
#define constraintsSet (IDA_mem->ida_constraintsSet)

#define epiccon        (IDA_mem->ida_epiccon)
#define maxnh          (IDA_mem->ida_maxnh)
#define maxnj          (IDA_mem->ida_maxnj)
#define maxnit         (IDA_mem->ida_maxnit)
#define lsoff          (IDA_mem->ida_lsoff)
#define steptol        (IDA_mem->ida_steptol)

#define sensi          (IDA_mem->ida_sensi)
#define Ns             (IDA_mem->ida_Ns)
#define resS           (IDA_mem->ida_resS)
#define phiS           (IDA_mem->ida_phiS)
#define ism            (IDA_mem->ida_ism)
#define ewtS           (IDA_mem->ida_ewtS)
#define ncfnS          (IDA_mem->ida_ncfnS)
#define nniS           (IDA_mem->ida_nniS)
#define nsetupsS       (IDA_mem->ida_nsetupsS)
#define yyS0           (IDA_mem->ida_yyS0)
#define ypS0           (IDA_mem->ida_ypS0)
#define delnewS        (IDA_mem->ida_delnewS)
#define savresS        (IDA_mem->ida_savresS)
#define yyS0new        (IDA_mem->ida_yyS0new)
#define ypS0new        (IDA_mem->ida_ypS0new)
#define eeS            (IDA_mem->ida_eeS)

/*
 * =================================================================
 * EXPORTED FUNCTIONS IMPLEMENTATION
 * =================================================================
 */

/*
 * -----------------------------------------------------------------
 * IDACalcIC
 * -----------------------------------------------------------------
 * IDACalcIC computes consistent initial conditions, given the 
 * user's initial guess for unknown components of yy0 and/or yp0.
 *
 * The return value is IDA_SUCCESS = 0 if no error occurred.
 *
 * The error return values (fully described in ida.h) are:
 *   IDA_MEM_NULL        ida_mem is NULL
 *   IDA_NO_MALLOC       ida_mem was not allocated
 *   IDA_ILL_INPUT       bad value for icopt, tout1, or id
 *   IDA_LINIT_FAIL      the linear solver linit routine failed
 *   IDA_BAD_EWT         zero value of some component of ewt
 *   IDA_RES_FAIL        res had a non-recoverable error
 *   IDA_FIRST_RES_FAIL  res failed recoverably on the first call
 *   IDA_LSETUP_FAIL     lsetup had a non-recoverable error
 *   IDA_LSOLVE_FAIL     lsolve had a non-recoverable error
 *   IDA_NO_RECOVERY     res, lsetup, or lsolve had a recoverable
 *                       error, but IDACalcIC could not recover
 *   IDA_CONSTR_FAIL     the inequality constraints could not be met
 *   IDA_LINESEARCH_FAIL the linesearch failed (on steptol test)
 *   IDA_CONV_FAIL       the Newton iterations failed to converge
 * -----------------------------------------------------------------
 */

int IDACalcIC(void *ida_mem, int icopt, realtype tout1)
{
  int ewtsetOK;
  int ier, nwt, nh, mxnh, icret, retval=0;
  int is;
  realtype tdist, troundoff, minid, hic, ypnorm;
  IDAMem IDA_mem;
  booleantype sensi_stg, sensi_sim;

  /* Check if IDA memory exists */

  if(ida_mem == NULL) {
    IDAProcessError(NULL, IDA_MEM_NULL, "IDAS", "IDACalcIC", MSG_NO_MEM);
    return(IDA_MEM_NULL);
  }
  IDA_mem = (IDAMem) ida_mem;

  /* Check if problem was malloc'ed */
  
  if(IDA_mem->ida_MallocDone == FALSE) {
    IDAProcessError(IDA_mem, IDA_NO_MALLOC, "IDAS", "IDACalcIC", MSG_NO_MALLOC);
    return(IDA_NO_MALLOC);
  }

  /* Check inputs to IDA for correctness and consistency */

  ier = IDAInitialSetup(IDA_mem);
  if(ier != IDA_SUCCESS) return(IDA_ILL_INPUT);
  IDA_mem->ida_SetupDone = TRUE;

  /* Check legality of input arguments, and set IDA memory copies. */

  if(icopt != IDA_YA_YDP_INIT && icopt != IDA_Y_INIT) {
    IDAProcessError(IDA_mem, IDA_ILL_INPUT, "IDAS", "IDACalcIC", MSG_IC_BAD_ICOPT);
    return(IDA_ILL_INPUT);
  }
  IDA_mem->ida_icopt = icopt;

  if(icopt == IDA_YA_YDP_INIT && (id == NULL)) {
    IDAProcessError(IDA_mem, IDA_ILL_INPUT, "IDAS", "IDACalcIC", MSG_IC_MISSING_ID);
    return(IDA_ILL_INPUT);
  }

  tdist = ABS(tout1 - tn);
  troundoff = TWO*uround*(ABS(tn) + ABS(tout1));    
  if(tdist < troundoff) {
    IDAProcessError(IDA_mem, IDA_ILL_INPUT, "IDAS", "IDACalcIC", MSG_IC_TOO_CLOSE);
    return(IDA_ILL_INPUT);
  }

  /* Are we computing sensitivities? */
  sensi_stg  = (sensi && (ism==IDA_STAGGERED));
  sensi_sim  = (sensi && (ism==IDA_SIMULTANEOUS));

  /* Allocate space and initialize temporary vectors */

  yy0 = N_VClone(ee);
  yp0 = N_VClone(ee);
  t0  = tn;
  N_VScale(ONE, phi[0], yy0);
  N_VScale(ONE, phi[1], yp0);

  if (sensi) {

    /* Allocate temporary space required for sensitivity IC: yyS0 and ypS0. */      
    yyS0 = N_VCloneVectorArray(Ns, ee);
    ypS0 = N_VCloneVectorArray(Ns, ee);
    
    /* Initialize sensitivity vector. */
    for (is=0; is<Ns; is++) {
      N_VScale(ONE, phiS[0][is], yyS0[is]);  
      N_VScale(ONE, phiS[1][is], ypS0[is]);  
    }
    
    /* Initialize work space vectors needed for sensitivities. */
    savresS = phiS[2];
    delnewS = phiS[3];
    yyS0new = phiS[4];
    ypS0new = eeS;
  }

  /* For use in the IDA_YA_YP_INIT case, set sysindex and tscale. */

  IDA_mem->ida_sysindex = 1;
  IDA_mem->ida_tscale   = tdist;
  if(icopt == IDA_YA_YDP_INIT) {
    minid = N_VMin(id);
    if(minid < ZERO) {
      IDAProcessError(IDA_mem, IDA_ILL_INPUT, "IDAS", "IDACalcIC", MSG_IC_BAD_ID);
      return(IDA_ILL_INPUT);
    }
    if(minid > HALF) IDA_mem->ida_sysindex = 0;
  }

  /* Set the test constant in the Newton convergence test */

  IDA_mem->ida_epsNewt = epiccon;

  /* Initializations: 
     cjratio = 1 (for use in direct linear solvers); 
     set nbacktr = 0; */

  cjratio = ONE;
  nbacktr = 0;

  /* Set hic, hh, cj, and mxnh. */

  hic = PT001*tdist;
  ypnorm = IDAWrmsNorm(IDA_mem, yp0, ewt, suppressalg);

  if (sensi_sim) 
    ypnorm = IDASensWrmsNormUpdate(IDA_mem, ypnorm, ypS0, ewtS, FALSE);

  if(ypnorm > HALF/hic) hic = HALF/ypnorm;
  if(tout1 < tn) hic = -hic;
  hh = hic;
  if(icopt == IDA_YA_YDP_INIT) {
    cj = ONE/hic;
    mxnh = maxnh;
  }
  else {
    cj = ZERO;
    mxnh = 1;
  }

  /* Loop over nwt = number of evaluations of ewt vector. */

  for(nwt = 1; nwt <= 2; nwt++) {
 
    /* Loop over nh = number of h values. */
    for(nh = 1; nh <= mxnh; nh++) {

      /* Call the IC nonlinear solver function. */
      retval = IDANlsIC(IDA_mem);

      /* Cut h and loop on recoverable IDA_YA_YDP_INIT failure; else break. */
      if(retval == IDA_SUCCESS) break;
      ncfn++;
      if(retval < 0) break;
      if(nh == mxnh) break;

      /* If looping to try again, reset yy0 and yp0 if not converging. */
      if(retval != IC_SLOW_CONVRG) {
        N_VScale(ONE, phi[0], yy0);
        N_VScale(ONE, phi[1], yp0);
        if (sensi_sim) {

          /* Reset yyS0 and ypS0. */
          /* Copy phiS[0] and phiS[1] into yyS0 and ypS0. */
          for (is=0; is<Ns; is++) {
            N_VScale(ONE, phiS[0][is], yyS0[is]);         
            N_VScale(ONE, phiS[1][is], ypS0[is]);         
          }
        }
      }
      hic *= PT1;
      cj = ONE/hic;
      hh = hic;
    }   /* End of nh loop */

    /* Break on failure */
    if(retval != IDA_SUCCESS) break;
    
    /* Reset ewt, save yy0, yp0 in phi, and loop. */
    ewtsetOK = efun(yy0, ewt, edata);
    if(ewtsetOK != 0) { 
      retval = IDA_BAD_EWT; 
      break; 
    }
    N_VScale(ONE, yy0, phi[0]);
    N_VScale(ONE, yp0, phi[1]);
    
    if (sensi_sim) {
      
      /* Reevaluate ewtS. */
      ewtsetOK = IDASensEwtSet(IDA_mem, yyS0, ewtS);
      if(ewtsetOK != 0) { 
        retval = IDA_BAD_EWT; 
        break; 
      }
      
      /* Save yyS0 and ypS0. */
      for (is=0; is<Ns; is++) {
            N_VScale(ONE, yyS0[is], phiS[0][is]);         
            N_VScale(ONE, ypS0[is], phiS[1][is]);        
      }
    }

  }   /* End of nwt loop */

  /* Load the optional outputs. */

  if(icopt == IDA_YA_YDP_INIT)   hused = hic;

  /* On any failure, free memory, print error message and return */

  if(retval != IDA_SUCCESS) {
    N_VDestroy(yy0);
    N_VDestroy(yp0);

    if(sensi) {
      N_VDestroyVectorArray(yyS0, Ns);
      N_VDestroyVectorArray(ypS0, Ns);
    }

    icret = IDAICFailFlag(IDA_mem, retval);
    return(icret);
  }

  /* Unless using the STAGGERED approach for sensitivities, return now */

  if (!sensi_stg) {

    N_VDestroy(yy0);
    N_VDestroy(yp0);

    if(sensi) {
      N_VDestroyVectorArray(yyS0, Ns);
      N_VDestroyVectorArray(ypS0, Ns);
    }

    return(IDA_SUCCESS);
  }

  /* Find consistent I.C. for sensitivities using a staggered approach */
 
  
  /* Evaluate res at converged y, needed for future evaluations of sens. RHS 
     If res() fails recoverably, treat it as a convergence failure and 
     attempt the step again */
        
  retval = res(t0, yy0, yp0, delta, user_data);
  nre++;
  if(retval < 0) 
    /* res function failed unrecoverably. */
    return(IDA_RES_FAIL);

  if(retval > 0) 
    /* res function failed recoverably but no recovery possible. */
    return(IDA_FIRST_RES_FAIL);
  
  /* Loop over nwt = number of evaluations of ewt vector. */
  for(nwt = 1; nwt <= 2; nwt++) {
 
    /* Loop over nh = number of h values. */
    for(nh = 1; nh <= mxnh; nh++) {

      retval = IDASensNlsIC(IDA_mem);
      if(retval == IDA_SUCCESS) break;

      /* Increment the number of the sensitivity related corrector convergence failures. */
      ncfnS++;

      if(retval < 0) break;
      if(nh == mxnh) break;

      /* If looping to try again, reset yyS0 and ypS0 if not converging. */
      if(retval != IC_SLOW_CONVRG) {
        for (is=0; is<Ns; is++) {
          N_VScale(ONE, phiS[0][is], yyS0[is]);  
          N_VScale(ONE, phiS[1][is], ypS0[is]);  
        }
      }
      hic *= PT1;
      cj = ONE/hic;
      hh = hic;
        
    }   /* End of nh loop */

    /* Break on failure */
    if(retval != IDA_SUCCESS) break;

    /* Since it was successful, reevaluate ewtS with the new values of yyS0, save 
       yyS0 and ypS0 in phiS[0] and phiS[1] and loop one more time to check and 
       maybe correct the  new sensitivities IC with respect to the new weights. */
    
    /* Reevaluate ewtS. */
    ewtsetOK = IDASensEwtSet(IDA_mem, yyS0, ewtS);
    if(ewtsetOK != 0) { 
      retval = IDA_BAD_EWT; 
      break; 
    }

    /* Save yyS0 and ypS0. */
    for (is=0; is<Ns; is++) {
      N_VScale(ONE, yyS0[is], phiS[0][is]);         
      N_VScale(ONE, ypS0[is], phiS[1][is]);        
    }

  }   /* End of nwt loop */


  /* Load the optional outputs. */
  if(icopt == IDA_YA_YDP_INIT)   hused = hic;

  /* Free temporary space */
  N_VDestroy(yy0);
  N_VDestroy(yp0);

  /* Here sensi is TRUE, so deallocate sensitivity temporary vectors. */
  N_VDestroyVectorArray(yyS0, Ns);
  N_VDestroyVectorArray(ypS0, Ns);


  /* On any failure, print message and return proper flag. */
  if(retval != IDA_SUCCESS) {
    icret = IDAICFailFlag(IDA_mem, retval);
    return(icret);
  }

  /* Otherwise return success flag. */

  return(IDA_SUCCESS);

}

/*
 * =================================================================
 * PRIVATE FUNCTIONS IMPLEMENTATION
 * =================================================================
 */

#define icopt          (IDA_mem->ida_icopt)
#define sysindex       (IDA_mem->ida_sysindex)
#define tscale         (IDA_mem->ida_tscale)
#define ynew           (IDA_mem->ida_ynew)
#define ypnew          (IDA_mem->ida_ypnew)
#define delnew         (IDA_mem->ida_delnew)
#define dtemp          (IDA_mem->ida_dtemp)

#define user_dataS     (IDA_mem->ida_user_dataS)
#define deltaS         (IDA_mem->ida_deltaS)

#define tmpS1          (IDA_mem->ida_tmpS1)
#define tmpS2          (IDA_mem->ida_tmpS2)
#define tmpS3          (IDA_mem->ida_tmpS3)

#define nrSe           (IDA_mem->ida_nrSe)
/*
 * -----------------------------------------------------------------
 * IDANlsIC
 * -----------------------------------------------------------------
 * IDANlsIC solves a nonlinear system for consistent initial 
 * conditions.  It calls IDANewtonIC to do most of the work.
 *
 * The return value is IDA_SUCCESS = 0 if no error occurred.
 * The error return values (positive) considered recoverable are:
 *  IC_FAIL_RECOV      if res, lsetup, or lsolve failed recoverably
 *  IC_CONSTR_FAILED   if the constraints could not be met
 *  IC_LINESRCH_FAILED if the linesearch failed (on steptol test)
 *  IC_CONV_FAIL       if the Newton iterations failed to converge
 *  IC_SLOW_CONVRG     if the iterations are converging slowly
 *                     (failed the convergence test, but showed
 *                     norm reduction or convergence rate < 1)
 * The error return values (negative) considered non-recoverable are:
 *  IDA_RES_FAIL       if res had a non-recoverable error
 *  IDA_FIRST_RES_FAIL if res failed recoverably on the first call
 *  IDA_LSETUP_FAIL    if lsetup had a non-recoverable error
 *  IDA_LSOLVE_FAIL    if lsolve had a non-recoverable error
 * -----------------------------------------------------------------
 */

static int IDANlsIC(IDAMem IDA_mem)
{
  int retval, nj, is;
  N_Vector tv1, tv2, tv3;
  booleantype sensi_sim;

  /* Are we computing sensitivities with the IDA_SIMULTANEOUS approach? */
  sensi_sim = (sensi && (ism==IDA_SIMULTANEOUS));

  tv1 = ee;
  tv2 = tempv2;
  tv3 = phi[2];
  
  /* Evaluate RHS. */
  retval = res(t0, yy0, yp0, delta, user_data);
  nre++;
  if(retval < 0) return(IDA_RES_FAIL);
  if(retval > 0) return(IDA_FIRST_RES_FAIL);

  /* Save the residual. */
  N_VScale(ONE, delta, savres);

  if(sensi_sim) {
    
    /*Evaluate sensitivity RHS and save it in savresS. */
    retval = resS(Ns, t0, 
                  yy0, yp0, delta,
                  yyS0, ypS0, deltaS,
                  user_dataS, tmpS1, tmpS2, tmpS3);
    nrSe++;
    if(retval < 0) return(IDA_RES_FAIL);
    if(retval > 0) return(IDA_FIRST_RES_FAIL);

    for(is=0; is<Ns; is++)
      N_VScale(ONE, deltaS[is], savresS[is]);
  }

  /* Loop over nj = number of linear solve Jacobian setups. */
  for(nj = 1; nj <= maxnj; nj++) {

    /* If there is a setup routine, call it. */
    if(setupNonNull) {
      nsetups++;
      retval = lsetup(IDA_mem, yy0, yp0, delta, tv1, tv2, tv3);
      if(retval < 0) return(IDA_LSETUP_FAIL);
      if(retval > 0) return(IC_FAIL_RECOV);
    }

    /* Call the Newton iteration routine, and return if successful.  */
    retval = IDANewtonIC(IDA_mem);
    if(retval == IDA_SUCCESS) return(IDA_SUCCESS);

    /* If converging slowly and lsetup is nontrivial, retry. */
    if(retval == IC_SLOW_CONVRG && setupNonNull) {
      N_VScale(ONE, savres, delta);

      if(sensi_sim)
        for(is=0; is<Ns; is++)
          N_VScale(ONE, savresS[is], deltaS[is]);

      continue;
    } else {
      return(retval);
    }

  }   /* End of nj loop */

  /* No convergence after maxnj tries; return with retval=IC_SLOW_CONVRG */
  return(retval);

}

/*
 * -----------------------------------------------------------------
 * IDANewtonIC
 * -----------------------------------------------------------------
 * IDANewtonIC performs the Newton iteration to solve for consistent
 * initial conditions.  It calls IDALineSrch within each iteration.
 * On return, savres contains the current residual vector.
 *
 * The return value is IDA_SUCCESS = 0 if no error occurred.
 * The error return values (positive) considered recoverable are:
 *  IC_FAIL_RECOV      if res or lsolve failed recoverably
 *  IC_CONSTR_FAILED   if the constraints could not be met
 *  IC_LINESRCH_FAILED if the linesearch failed (on steptol test)
 *  IC_CONV_FAIL       if the Newton iterations failed to converge
 *  IC_SLOW_CONVRG     if the iterations appear to be converging slowly.
 *                     They failed the convergence test, but showed 
 *                     an overall norm reduction (by a factor of < 0.1)
 *                     or a convergence rate <= ICRATEMAX).
 * The error return values (negative) considered non-recoverable are:
 *  IDA_RES_FAIL   if res had a non-recoverable error
 *  IDA_LSOLVE_FAIL      if lsolve had a non-recoverable error
 * -----------------------------------------------------------------
 */

static int IDANewtonIC(IDAMem IDA_mem)
{
  int retval, mnewt, is;
  realtype delnorm, fnorm, fnorm0, oldfnrm, rate;
  booleantype sensi_sim;

  /* Are we computing sensitivities with the IDA_SIMULTANEOUS approach? */
  sensi_sim = (sensi && (ism==IDA_SIMULTANEOUS));

  /* Set pointer for vector delnew */
  delnew = phi[2];

  /* Call the linear solve function to get the Newton step, delta. */
  retval = lsolve(IDA_mem, delta, ewt, yy0, yp0, savres);
  if(retval < 0) return(IDA_LSOLVE_FAIL);
  if(retval > 0) return(IC_FAIL_RECOV);

  /* Compute the norm of the step. */
  fnorm = IDAWrmsNorm(IDA_mem, delta, ewt, FALSE);

  /* Call the lsolve function to get correction vectors deltaS. */
  if (sensi_sim) {
    for(is=0;is<Ns;is++) {
      retval = lsolve(IDA_mem, deltaS[is], ewtS[is], yy0, yp0, savres);
      if(retval < 0) return(IDA_LSOLVE_FAIL);
      if(retval > 0) return(IC_FAIL_RECOV);
    }
    /* Update the norm of delta. */
    fnorm = IDASensWrmsNormUpdate(IDA_mem, fnorm, deltaS, ewtS, FALSE);
  }

  /* Test for convergence. Return now if the norm is small. */
  if(sysindex == 0) fnorm *= tscale*ABS(cj);
  if(fnorm <= epsNewt) return(IDA_SUCCESS);
  fnorm0 = fnorm;

  /* Initialize rate to avoid compiler warning message */
  rate = ZERO;

  /* Newton iteration loop */

  for(mnewt = 0; mnewt < maxnit; mnewt++) {

    nni++;
    delnorm = fnorm;
    oldfnrm = fnorm;

    /* Call the Linesearch function and return if it failed. */
    retval = IDALineSrch(IDA_mem, &delnorm, &fnorm);
    if(retval != IDA_SUCCESS) return(retval);

    /* Set the observed convergence rate and test for convergence. */
    rate = fnorm/oldfnrm;
    if(fnorm <= epsNewt) return(IDA_SUCCESS);

    /* If not converged, copy new step vector, and loop. */
    N_VScale(ONE, delnew, delta);

    if(sensi_sim) {
      /* Update the iteration's step for sensitivities. */
      for(is=0; is<Ns; is++)
        N_VScale(ONE, delnewS[is], deltaS[is]);
    }

  }   /* End of Newton iteration loop */

  /* Return either IC_SLOW_CONVRG or recoverable fail flag. */
  if(rate <= ICRATEMAX || fnorm < PT1*fnorm0) return(IC_SLOW_CONVRG);
  return(IC_CONV_FAIL);
}

/*
 * -----------------------------------------------------------------
 * IDALineSrch
 * -----------------------------------------------------------------
 * IDALineSrch performs the Linesearch algorithm with the 
 * calculation of consistent initial conditions.
 *
 * On entry, yy0 and yp0 are the current values of y and y', the 
 * Newton step is delta, the current residual vector F is savres,
 * delnorm is WRMS-norm(delta), and fnorm is the norm of the vector
 * J-inverse F.
 *
 * On a successful return, yy0, yp0, and savres have been updated, 
 * delnew contains the current value of J-inverse F, and fnorm is
 * WRMS-norm(delnew).
 *
 * The return value is IDA_SUCCESS = 0 if no error occurred.
 * The error return values (positive) considered recoverable are:
 *  IC_FAIL_RECOV      if res or lsolve failed recoverably
 *  IC_CONSTR_FAILED   if the constraints could not be met
 *  IC_LINESRCH_FAILED if the linesearch failed (on steptol test)
 * The error return values (negative) considered non-recoverable are:
 *  IDA_RES_FAIL   if res had a non-recoverable error
 *  IDA_LSOLVE_FAIL      if lsolve had a non-recoverable error
 * -----------------------------------------------------------------
 */

static int IDALineSrch(IDAMem IDA_mem, realtype *delnorm, realtype *fnorm)
{
  booleantype conOK;
  int retval, is;
  realtype f1norm, fnormp, f1normp, ratio, lambda, minlam, slpi;
  N_Vector mc;
  booleantype sensi_sim;

  /* Initialize work space pointers, f1norm, ratio.
     (Use of mc in constraint check does not conflict with ypnew.) */
  mc = ee;
  dtemp = phi[3];
  ynew = tempv2;
  ypnew = ee;
  f1norm = (*fnorm)*(*fnorm)*HALF;
  ratio = ONE;

  /* If there are constraints, check and reduce step if necessary. */
  if(constraintsSet) {

    /* Update y and check constraints. */
    IDANewy(IDA_mem);
    conOK = N_VConstrMask(constraints, ynew, mc);

    if(!conOK) {
      /* Not satisfied.  Compute scaled step to satisfy constraints. */
      N_VProd(mc, delta, dtemp);
      ratio = PT99*N_VMinQuotient(yy0, dtemp);
      (*delnorm) *= ratio;
      if((*delnorm) <= steptol) return(IC_CONSTR_FAILED);
      N_VScale(ratio, delta, delta);
    }

  } /* End of constraints check */

  slpi = -TWO*f1norm*ratio;
  minlam = steptol/(*delnorm);
  lambda = ONE;

  /* Are we computing sensitivities with the IDA_SIMULTANEOUS approach? */
  sensi_sim = (sensi && (ism==IDA_SIMULTANEOUS));

  /* In IDA_Y_INIT case, set ypnew = yp0 (fixed) for linesearch. */
  if(icopt == IDA_Y_INIT) {
    N_VScale(ONE, yp0, ypnew);

    /* do the same for sensitivities. */
    if(sensi_sim) {
      for(is=0; is<Ns; is++)
        N_VScale(ONE, ypS0[is], ypS0new[is]);
    }
  }

  /* Loop on linesearch variable lambda. */

  loop {

    /* Get new (y,y') = (ynew,ypnew) and norm of new function value. */
    IDANewyyp(IDA_mem, lambda);
    retval = IDAfnorm(IDA_mem, &fnormp);
    if(retval != IDA_SUCCESS) return(retval);

    /* If lsoff option is on, break out. */
    if(lsoff) break;

    /* Do alpha-condition test. */
    f1normp = fnormp*fnormp*HALF;
    if(f1normp <= f1norm + ALPHALS*slpi*lambda) break;
    if(lambda < minlam) return(IC_LINESRCH_FAILED);
    lambda /= TWO;
    nbacktr++;

  }  /* End of breakout linesearch loop */

  /* Update yy0, yp0. */
  N_VScale(ONE, ynew, yy0);

  if(sensi_sim) {
    /* Update yyS0 and ypS0. */
    for(is=0; is<Ns; is++)
      N_VScale(ONE, yyS0new[is], yyS0[is]);
  }

  if(icopt == IDA_YA_YDP_INIT) {
    N_VScale(ONE, ypnew, yp0);
    
    if(sensi_sim)
      for(is=0; is<Ns; is++)
        N_VScale(ONE, ypS0new[is], ypS0[is]);

  }
  /* Update fnorm, then return. */
  *fnorm = fnormp;
  return(IDA_SUCCESS);

}

/*
 * -----------------------------------------------------------------
 * IDAfnorm
 * -----------------------------------------------------------------
 * IDAfnorm computes the norm of the current function value, by
 * evaluating the DAE residual function, calling the linear 
 * system solver, and computing a WRMS-norm.
 *
 * On return, savres contains the current residual vector F, and
 * delnew contains J-inverse F.
 *
 * The return value is IDA_SUCCESS = 0 if no error occurred, or
 *  IC_FAIL_RECOV    if res or lsolve failed recoverably, or
 *  IDA_RES_FAIL     if res had a non-recoverable error, or
 *  IDA_LSOLVE_FAIL  if lsolve had a non-recoverable error.
 * -----------------------------------------------------------------
 */

static int IDAfnorm(IDAMem IDA_mem, realtype *fnorm)
{
  int retval, is;

  /* Get residual vector F, return if failed, and save F in savres. */
  retval = res(t0, ynew, ypnew, delnew, user_data);
  nre++;
  if(retval < 0) return(IDA_RES_FAIL);
  if(retval > 0) return(IC_FAIL_RECOV);

  N_VScale(ONE, delnew, savres);

  /* Call the linear solve function to get J-inverse F; return if failed. */
  retval = lsolve(IDA_mem, delnew, ewt, ynew, ypnew, savres);
  if(retval < 0) return(IDA_LSOLVE_FAIL);
  if(retval > 0) return(IC_FAIL_RECOV);

  /* Compute the WRMS-norm. */
  *fnorm = IDAWrmsNorm(IDA_mem, delnew, ewt, FALSE);


  /* Are we computing SENSITIVITIES with the IDA_SIMULTANEOUS approach? */

  if(sensi && (ism==IDA_SIMULTANEOUS)) {

    /* Evaluate the residual for sensitivities. */
    retval = resS(Ns, t0, 
                  ynew, ypnew, savres,
                  yyS0new, ypS0new, delnewS,
                  user_dataS, tmpS1, tmpS2, tmpS3);
    nrSe++;
    if(retval < 0) return(IDA_RES_FAIL);
    if(retval > 0) return(IC_FAIL_RECOV);

    /* Save delnewS in savresS. */
    for(is=0; is<Ns; is++)
      N_VScale(ONE, delnewS[is], savresS[is]);

    /* Call the linear solve function to get J-inverse deltaS. */
    for(is=0; is<Ns; is++) {

      retval = lsolve(IDA_mem, delnewS[is], ewtS[is], ynew, ypnew, savres);
      if(retval < 0) return(IDA_LSOLVE_FAIL);
      if(retval > 0) return(IC_FAIL_RECOV);
    }
      
    /* Include sensitivities in norm. */
    *fnorm = IDASensWrmsNormUpdate(IDA_mem, *fnorm, delnewS, ewtS, FALSE);
  }

  /* Rescale norm if index = 0. */
  if(sysindex == 0) (*fnorm) *= tscale*ABS(cj);

  return(IDA_SUCCESS);

}

/*
 * -----------------------------------------------------------------
 * IDANewyyp
 * -----------------------------------------------------------------
 * IDANewyyp updates the vectors ynew and ypnew from yy0 and yp0,
 * using the current step vector lambda*delta, in a manner
 * depending on icopt and the input id vector.
 *
 * The return value is always IDA_SUCCESS = 0.
 * -----------------------------------------------------------------
 */

static int IDANewyyp(IDAMem IDA_mem, realtype lambda)
{
  int retval;
  
  retval = IDA_SUCCESS;

  /* IDA_YA_YDP_INIT case: ynew  = yy0 - lambda*delta    where id_i = 0
                           ypnew = yp0 - cj*lambda*delta where id_i = 1. */
  if(icopt == IDA_YA_YDP_INIT) {

    N_VProd(id, delta, dtemp);
    N_VLinearSum(ONE, yp0, -cj*lambda, dtemp, ypnew);
    N_VLinearSum(ONE, delta, -ONE, dtemp, dtemp);
    N_VLinearSum(ONE, yy0, -lambda, dtemp, ynew);

  }else if(icopt == IDA_Y_INIT) {

    /* IDA_Y_INIT case: ynew = yy0 - lambda*delta. (ypnew = yp0 preset.) */
    N_VLinearSum(ONE, yy0, -lambda, delta, ynew);
  }

  if(sensi && (ism==IDA_SIMULTANEOUS))
    retval = IDASensNewyyp(IDA_mem, lambda);
  
  return(retval);

}

/*
 * -----------------------------------------------------------------
 * IDANewy
 * -----------------------------------------------------------------
 * IDANewy updates the vector ynew from yy0,
 * using the current step vector delta, in a manner
 * depending on icopt and the input id vector.
 *
 * The return value is always IDA_SUCCESS = 0.
 * -----------------------------------------------------------------
 */

static int IDANewy(IDAMem IDA_mem)
{
  
  /* IDA_YA_YDP_INIT case: ynew = yy0 - delta    where id_i = 0. */
  if(icopt == IDA_YA_YDP_INIT) {
    N_VProd(id, delta, dtemp);
    N_VLinearSum(ONE, delta, -ONE, dtemp, dtemp);
    N_VLinearSum(ONE, yy0, -ONE, dtemp, ynew);
    return(IDA_SUCCESS);
  }

  /* IDA_Y_INIT case: ynew = yy0 - delta. */
  N_VLinearSum(ONE, yy0, -ONE, delta, ynew);
  return(IDA_SUCCESS);

}
/*
 * -----------------------------------------------------------------
 * Sensitivity I.C. functions
 * -----------------------------------------------------------------
 */

/* 
 * -----------------------------------------------------------------
 * IDASensNlsIC
 * -----------------------------------------------------------------
 * IDASensNlsIC solves nonlinear systems forsensitivities consistent 
 * initial conditions.  It mainly relies on IDASensNewtonIC.
 *
 * The return value is IDA_SUCCESS = 0 if no error occurred.
 * The error return values (positive) considered recoverable are:
 *  IC_FAIL_RECOV      if res, lsetup, or lsolve failed recoverably
 *  IC_CONSTR_FAILED   if the constraints could not be met
 *  IC_LINESRCH_FAILED if the linesearch failed (on steptol test)
 *  IC_CONV_FAIL       if the Newton iterations failed to converge
 *  IC_SLOW_CONVRG     if the iterations are converging slowly
 *                     (failed the convergence test, but showed
 *                     norm reduction or convergence rate < 1)
 * The error return values (negative) considered non-recoverable are:
 *  IDA_RES_FAIL       if res had a non-recoverable error
 *  IDA_FIRST_RES_FAIL if res failed recoverably on the first call
 *  IDA_LSETUP_FAIL    if lsetup had a non-recoverable error
 *  IDA_LSOLVE_FAIL    if lsolve had a non-recoverable error
 * -----------------------------------------------------------------
 */
static int IDASensNlsIC(IDAMem IDA_mem)
{
  int retval;
  int is, nj;

  retval = resS(Ns, t0, 
                yy0,  yp0,  delta,
                yyS0, ypS0, deltaS,
                user_dataS, tmpS1, tmpS2, tmpS3);
  nrSe++;
  if(retval < 0) return(IDA_RES_FAIL);
  if(retval > 0) return(IDA_FIRST_RES_FAIL);
  
  /* Save deltaS */
  for(is=0; is<Ns; is++) N_VScale(ONE, deltaS[is], savresS[is]);

  /* Loop over nj = number of linear solve Jacobian setups. */

  for(nj = 1; nj <= 2; nj++) {

    /* Call the Newton iteration routine */
    retval = IDASensNewtonIC(IDA_mem);
    if(retval == IDA_SUCCESS) return(IDA_SUCCESS);

    /* If converging slowly and lsetup is nontrivial and this is the first pass, 
       update Jacobian and retry. */
    if(retval == IC_SLOW_CONVRG && setupNonNull && nj==1) {

      /* Restore deltaS. */
      for(is=0; is<Ns; is++) N_VScale(ONE, savresS[is], deltaS[is]);

      nsetupsS++;
      retval = lsetup(IDA_mem, yy0, yp0, delta, tmpS1, tmpS2, tmpS3);
      if(retval < 0) return(IDA_LSETUP_FAIL);
      if(retval > 0) return(IC_FAIL_RECOV);

      continue;
    } else {
      return(retval);
    }
  }

  return(IDA_SUCCESS);
}

/*
 * -----------------------------------------------------------------
 * IDASensNewtonIC
 * -----------------------------------------------------------------
 * IDANewtonIC performs the Newton iteration to solve for 
 * sensitivities consistent initial conditions.  It calls 
 * IDASensLineSrch within each iteration.
 * On return, savresS contains the current residual vectors.
 *
 * The return value is IDA_SUCCESS = 0 if no error occurred.
 * The error return values (positive) considered recoverable are:
 *  IC_FAIL_RECOV      if res or lsolve failed recoverably
 *  IC_CONSTR_FAILED   if the constraints could not be met
 *  IC_LINESRCH_FAILED if the linesearch failed (on steptol test)
 *  IC_CONV_FAIL       if the Newton iterations failed to converge
 *  IC_SLOW_CONVRG     if the iterations appear to be converging slowly.
 *                     They failed the convergence test, but showed 
 *                     an overall norm reduction (by a factor of < 0.1)
 *                     or a convergence rate <= ICRATEMAX).
 * The error return values (negative) considered non-recoverable are:
 *  IDA_RES_FAIL   if res had a non-recoverable error
 *  IDA_LSOLVE_FAIL      if lsolve had a non-recoverable error
 * -----------------------------------------------------------------
 */
static int IDASensNewtonIC(IDAMem IDA_mem)
{
  int retval, is, mnewt;
  realtype delnorm, fnorm, fnorm0, oldfnrm, rate;

  for(is=0;is<Ns;is++) {
   
    /* Call the linear solve function to get the Newton step, delta. */
    retval = lsolve(IDA_mem, deltaS[is], ewtS[is],  yy0, yp0, delta);
    if(retval < 0) return(IDA_LSOLVE_FAIL);
    if(retval > 0) return(IC_FAIL_RECOV);

  }
    /* Compute the norm of the step and return if it is small enough */
  fnorm = IDASensWrmsNorm(IDA_mem, deltaS, ewtS, FALSE);
  if(sysindex == 0) fnorm *= tscale*ABS(cj);
  if(fnorm <= epsNewt) return(IDA_SUCCESS);
  fnorm0 = fnorm;

  rate = ZERO;

  /* Newton iteration loop */
  for(mnewt = 0; mnewt < maxnit; mnewt++) {

    nniS++;
    delnorm = fnorm;
    oldfnrm = fnorm;
      
    /* Call the Linesearch function and return if it failed. */
    retval = IDASensLineSrch(IDA_mem, &delnorm, &fnorm);
    if(retval != IDA_SUCCESS) return(retval);
      
    /* Set the observed convergence rate and test for convergence. */
    rate = fnorm/oldfnrm;
    if(fnorm <= epsNewt) return(IDA_SUCCESS);
    
    /* If not converged, copy new step vectors, and loop. */
    for(is=0; is<Ns; is++) N_VScale(ONE, delnewS[is], deltaS[is]);
    
  }   /* End of Newton iteration loop */

  /* Return either IC_SLOW_CONVRG or recoverable fail flag. */
  if(rate <= ICRATEMAX || fnorm < PT1*fnorm0) return(IC_SLOW_CONVRG);
  return(IC_CONV_FAIL);
}

/*
 * -----------------------------------------------------------------
 * IDASensLineSrch
 * -----------------------------------------------------------------
 * IDASensLineSrch performs the Linesearch algorithm with the 
 * calculation of consistent initial conditions for sensitivities
 * systems.
 *
 * On entry, yyS0 and ypS0 contain the current values, the Newton 
 * steps are contained in deltaS, the current residual vectors FS are
 * savresS, delnorm is sens-WRMS-norm(deltaS), and fnorm is
 * max { WRMS-norm( J-inverse FS[is] ) : is=1,2,...,Ns } 
 *
 * On a successful return, yy0, yp0, and savres have been updated, 
 * delnew contains the current values of J-inverse FS, and fnorm is
 * max { WRMS-norm(delnewS[is]) : is = 1,2,...Ns }
 *
 * The return value is IDA_SUCCESS = 0 if no error occurred.
 * The error return values (positive) considered recoverable are:
 *  IC_FAIL_RECOV      if res or lsolve failed recoverably
 *  IC_CONSTR_FAILED   if the constraints could not be met
 *  IC_LINESRCH_FAILED if the linesearch failed (on steptol test)
 * The error return values (negative) considered non-recoverable are:
 *  IDA_RES_FAIL   if res had a non-recoverable error
 *  IDA_LSOLVE_FAIL      if lsolve had a non-recoverable error
 * -----------------------------------------------------------------
 */

static int IDASensLineSrch(IDAMem IDA_mem, realtype *delnorm, realtype *fnorm)
{
  int is, retval;
  realtype f1norm, fnormp, f1normp, slpi, minlam;
  realtype lambda, ratio;
  
  f1norm = (*fnorm)*(*fnorm)*HALF;
  
  /* Initialize local variables. */
  ratio = ONE;
  slpi = -TWO*f1norm*ratio;
  minlam = steptol/(*delnorm);
  lambda = ONE;

  loop {
    /* Get new iteration in (ySnew, ypSnew). */
    IDASensNewyyp(IDA_mem, lambda);

    /* Get the norm of new function value. */
    retval = IDASensfnorm(IDA_mem, &fnormp);
    if (retval!=IDA_SUCCESS) return retval;

    /* If lsoff option is on, break out. */
    if(lsoff) break;

    /* Do alpha-condition test. */
    f1normp = fnormp*fnormp*HALF;
    if(f1normp <= f1norm + ALPHALS*slpi*lambda) break;
    if(lambda < minlam) return(IC_LINESRCH_FAILED);
    lambda /= TWO;
    nbacktr++;
  }
  
  /* Update yyS0, ypS0 and fnorm and return. */
  for(is=0; is<Ns; is++) {
    N_VScale(ONE, yyS0new[is], yyS0[is]);
  }

  if (icopt == IDA_YA_YDP_INIT)
    for(is=0; is<Ns; is++) 
      N_VScale(ONE, ypS0new[is], ypS0[is]);

  *fnorm = fnormp;
  return(IDA_SUCCESS);
}

/*
 * -----------------------------------------------------------------
 * IDASensfnorm
 * -----------------------------------------------------------------
 * IDASensfnorm computes the norm of the current function value, by
 * evaluating the sensitivity residual function, calling the linear 
 * system solver, and computing a WRMS-norm.
 *
 * On return, savresS contains the current residual vectors FS, and
 * delnewS contains J-inverse FS.
 *
 * The return value is IDA_SUCCESS = 0 if no error occurred, or
 *  IC_FAIL_RECOV    if res or lsolve failed recoverably, or
 *  IDA_RES_FAIL     if res had a non-recoverable error, or
 *  IDA_LSOLVE_FAIL  if lsolve had a non-recoverable error.
 * -----------------------------------------------------------------
 */

static int IDASensfnorm(IDAMem IDA_mem, realtype *fnorm)
{
  int is, retval;
  
  /* Get sensitivity residual */
  retval = resS(Ns, t0, 
                yy0,  yp0,  delta,
                yyS0new, ypS0new, delnewS,
                user_dataS, tmpS1, tmpS2, tmpS3);
  nrSe++;
  if(retval < 0) return(IDA_RES_FAIL);
  if(retval > 0) return(IC_FAIL_RECOV);
  
  for(is=0; is<Ns; is++) N_VScale(ONE, delnewS[is], savresS[is]);
  
  /* Call linear solve function */
  for(is=0; is<Ns; is++) {
    
    retval = lsolve(IDA_mem, delnewS[is], ewtS[is],  yy0, yp0, delta);
    if(retval < 0) return(IDA_LSOLVE_FAIL);
    if(retval > 0) return(IC_FAIL_RECOV);
  }

  /* Compute the WRMS-norm; rescale if index = 0. */
  *fnorm = IDASensWrmsNorm(IDA_mem, delnewS, ewtS, FALSE);
  if(sysindex == 0) (*fnorm) *= tscale*ABS(cj);  

  return(IDA_SUCCESS);
}

/*
 * -----------------------------------------------------------------
 * IDASensNewyyp
 * -----------------------------------------------------------------
 * IDASensNewyyp computes the Newton updates for each of the 
 * sensitivities systems using the current step vector lambda*delta, 
 * in a manner depending on icopt and the input id vector.
 *
 * The return value is always IDA_SUCCESS = 0.
 * -----------------------------------------------------------------
 */

static int IDASensNewyyp(IDAMem IDA_mem, realtype lambda)
{
  int is;

  if(icopt == IDA_YA_YDP_INIT) {

  /* IDA_YA_YDP_INIT case: 
     - ySnew  = yS0  - lambda*deltaS    where id_i = 0
     - ypSnew = ypS0 - cj*lambda*delta  where id_i = 1. */    

    for(is=0; is<Ns; is++) {
      
      /* It is ok to use dtemp as temporary vector here. */
      N_VProd(id, deltaS[is], dtemp);
      N_VLinearSum(ONE, ypS0[is], -cj*lambda, dtemp, ypS0new[is]);
      N_VLinearSum(ONE, deltaS[is], -ONE, dtemp, dtemp);
      N_VLinearSum(ONE, yyS0[is], -lambda, dtemp, yyS0new[is]);
    } /* end loop is */
  }else { 

    /* IDA_Y_INIT case: 
       - ySnew = yS0 - lambda*deltaS. (ypnew = yp0 preset.) */

    for(is=0; is<Ns; is++)
      N_VLinearSum(ONE, yyS0[is], -lambda, deltaS[is], yyS0new[is]);
  } /* end loop is */
  return(IDA_SUCCESS);
}

/*
 * -----------------------------------------------------------------
 * IDAICFailFlag
 * -----------------------------------------------------------------
 * IDAICFailFlag prints a message and sets the IDACalcIC return
 * value appropriate to the flag retval returned by IDANlsIC.
 * -----------------------------------------------------------------
 */

static int IDAICFailFlag(IDAMem IDA_mem, int retval)
{

  /* Depending on retval, print error message and return error flag. */
  switch(retval) {

    case IDA_RES_FAIL:
      IDAProcessError(IDA_mem, IDA_RES_FAIL, "IDAS", "IDACalcIC", MSG_IC_RES_NONREC);
      return(IDA_RES_FAIL);

    case IDA_FIRST_RES_FAIL:
      IDAProcessError(IDA_mem, IDA_FIRST_RES_FAIL, "IDAS", "IDACalcIC", MSG_IC_RES_FAIL);
      return(IDA_FIRST_RES_FAIL);

    case IDA_LSETUP_FAIL:
      IDAProcessError(IDA_mem, IDA_LSETUP_FAIL, "IDAS", "IDACalcIC", MSG_IC_SETUP_FAIL);
      return(IDA_LSETUP_FAIL);

    case IDA_LSOLVE_FAIL:  
      IDAProcessError(IDA_mem, IDA_LSOLVE_FAIL, "IDAS", "IDACalcIC", MSG_IC_SOLVE_FAIL);
      return(IDA_LSOLVE_FAIL);

    case IC_FAIL_RECOV:
      IDAProcessError(IDA_mem, IDA_NO_RECOVERY, "IDAS", "IDACalcIC", MSG_IC_NO_RECOVERY);
      return(IDA_NO_RECOVERY);

    case IC_CONSTR_FAILED: 
      IDAProcessError(IDA_mem, IDA_CONSTR_FAIL, "IDAS", "IDACalcIC", MSG_IC_FAIL_CONSTR);
      return(IDA_CONSTR_FAIL);

    case IC_LINESRCH_FAILED:  
      IDAProcessError(IDA_mem, IDA_LINESEARCH_FAIL, "IDAS", "IDACalcIC", MSG_IC_FAILED_LINS);
      return(IDA_LINESEARCH_FAIL);

    case IC_CONV_FAIL:
      IDAProcessError(IDA_mem, IDA_CONV_FAIL, "IDAS", "IDACalcIC", MSG_IC_CONV_FAILED);
      return(IDA_CONV_FAIL);

    case IC_SLOW_CONVRG: 
      IDAProcessError(IDA_mem, IDA_CONV_FAIL, "IDAS", "IDACalcIC", MSG_IC_CONV_FAILED);
      return(IDA_CONV_FAIL);

    case IDA_BAD_EWT:
      IDAProcessError(IDA_mem, IDA_BAD_EWT, "IDAS", "IDACalcIC", MSG_IC_BAD_EWT);
      return(IDA_BAD_EWT);

  }
  return -99;
}
