6.4.4. Using IDAS for Forward Sensitivity Analysis
This chapter describes the use of IDAS to compute solution sensitivities using
forward sensitivity analysis. One of our main guiding principles was to design
the IDAS user interface for forward sensitivity analysis as an extension of
that for IVP integration. Assuming a user main program and userdefined support
routines for IVP integration have already been defined, in order to perform
forward sensitivity analysis the user only has to insert a few more calls into
the main program and (optionally) define an additional routine which computes
the residual of the sensitivity systems (6.11). The only
departure from this philosophy is due to the IDAResFn
type definition.
Without changing the definition of this type, the only way to pass values of the
problem parameters to the ODE residual function is to require the user
data structure f_data
to contain a pointer to the array of real parameters
\(p\).
IDAS uses various constants for both input and output. These are defined as needed in this chapter, but for convenience are also listed separately in §6.5.
We begin with a brief overview, in the form of a skeleton user program. Following that are detailed descriptions of the interface to the various usercallable routines and of the usersupplied routines that were not already described in §6.4.1 or §6.4.2.
6.4.4.1. A skeleton of the user’s main program
The following is a skeleton of the user’s main program (or calling program) as
an application of IDAS. The user program is to have these steps in the order
indicated, unless otherwise noted. For the sake of brevity, we defer many of the
details to the later sections. As in §6.4.1.2,
most steps are independent of the N_Vector
, SUNMatrix
,
SUNLinearSolver
, and SUNNonlinearSolver
implementations used. For the
steps that are not, refer to Chapters §8, §9,
§10, §11 for the specific name of the
function to be called or macro to be referenced.
First, note that no additional header files need be included for forward sensitivity analysis beyond those for IVP solution §6.4.1.2.
Steps that are unchanged from the user main program skeleton in §6.4.1.2 are grayed out and new or modified steps are in bold.
Initialize parallel or multithreaded environment
Create the SUNDIALS context object
Set the vector of initial values
Create matrix object
Create linear solver object
Create nonlinear solver object
Create IDAS object
Initialize IDAS solver
Specify integration tolerances
Attach linear solver
Set linear solver optional inputs
Attach nonlinear solver
Set nonlinear solver optional inputs
Initialize quadrature integration
If the quadrature is not sensitivitydependent, initialize the quadrature integration as described in §6.4.2. For integrating a problem where the quadrature depends on the forward sensitivities see §6.4.4.4.
Set the sensitivity initial values
Call
N_VCloneVectorArray()
to createN_Vector
arraysyS0
andypS0
to hold the initial values for the sensitivity vectors of \(y\) and sensitivity derivative vectors of \(\dot{y}\), respectively.yS0 = N_VCloneVectorArray(Ns, y0); ypS0 = N_VCloneVectorArray(Ns, y0);
where
Ns
is the number of parameters with respect to which sensitivities are to be computed andy0
serves only to provide anN_Vector
template for cloning.Then, load initial values for each sensitivity vector
yS0[i]
and sensitivity derivative vectorypS0[i]
fori = 0,...,N_s1
.Activate sensitivity calculations
Call
IDASensInit()
to activate forward sensitivity computations and allocate internal memory for IDAS related to sensitivity calculations.If a sensitivity residual function is not provided to
IDASensInit()
, thenIDASetSensParams()
must be called afterIDASensInit()
and beforeIDASolve()
to provide the array of problem parameters with respect to which the sensitivities are computed. This array must also be attached to the “user data” pointer set withIDASetUserData()
. Optionally, an array of scaling factors for differencequotient residual computations and a mask array to select which parameters with respect to which the sensitivities are computed may also be provided toIDASetSensParams()
.Set sensitivity integration tolerances (optional)
Call
IDASensSStolerances()
orIDASensSVtolerances()
to set the sensitivity integration tolerances orIDASensEEtolerances()
to have IDAS estimate tolerances for sensitivity variables based on the tolerances supplied for states variables.If sensitivity tolerances are estimated by IDAS, the results will be more accurate if order of magnitude is provided by setting the
pbar
input toIDASetSensParams()
.Create sensitivity nonlinear solver
If using a nondefault nonlinear solver (see §6.4.4.2.3), then create the desired nonlinear solver object by calling the appropriate constructor function defined by the particular
SUNNonlinearSolver
implementation e.g.,NLSSens = SUNNonlinSol_***Sens(...);
for the
IDA_SIMULTANEOUS
orIDA_STAGGERED
options***
is the name of the nonlinear solver and...
are constructor specific arguments (see §11 for details).Attach the sensitivity nonlinear solver
If using a nondefault nonlinear solver, then initialize the nonlinear solver interface by attaching the nonlinear solver object by calling
IDASetNonlinearSolverSensSim()
when using theIDA_SIMULTANEOUS
corrector method,IDASetNonlinearSolverSensStg()
when using theIDA_STAGGERED
corrector method (see §6.4.4.2.3 for details).Set sensitivity nonlinear solver optional inputs
Call the appropriate set functions for the selected nonlinear solver module to change optional inputs specific to that nonlinear solver. These must be called after
IDASensInit()
if using the default nonlinear solver or after attaching a new nonlinear solver to IDAS, otherwise the optional inputs will be overridden by IDAS defaults. See §11 for more information on optional inputs.Specify rootfinding problem
Set optional inputs
Call
IDASetSens*
routines to change from their default values any optional inputs that control the behavior of IDAS in computing forward sensitivities. See §6.4.4.2.7 for details.Correct initial values
Advance solution in time
Extract sensitivity solution
After each successful return from
IDASolve()
, the solution of the original IVP is available in they
argument ofIDASolve()
, while the sensitivity solution can be extracted intoyS
andypS
(which can be the same asyS0
andypS0
) by calling one of the routinesIDAGetSens()
,IDAGetSens1()
,IDAGetSensDky()
, orIDAGetSensDky1()
.Get optional outputs
Deallocate memory
Upon completion of the integration, deallocate memory for the vectors
yS0
andyps0
usingN_VDestroyVectorArray()
.Finalize MPI, if used
6.4.4.2. Usercallable routines for forward sensitivity analysis
This section describes the IDAS functions, in addition to those presented in §6.4.1.3, that are called by the user to setup and solve a forward sensitivity problem.
6.4.4.2.1. Forward sensitivity initialization and deallocation functions
Activation of forward sensitivity computation is done by calling
IDASensInit()
. The form of the call is as follows:

int IDASensInit(void *ida_mem, int Ns, int ism, IDASensResFn fS, N_Vector *yS0, N_Vector *ypS0)
The routine
IDASensInit()
activates forward sensitivity computations and allocates internal memory related to sensitivity calculations. Arguments:
ida_mem
– pointer to the IDAS memory block returned byIDACreate()
.Ns
– the number of sensitivities to be computed.ism
– forward sensitivity analysis!correction strategies a flag used to select the sensitivity solution method. Its value can beIDA_SIMULTANEOUS
orIDA_STAGGERED
:In the
IDA_SIMULTANEOUS
approach, the state and sensitivity variables are corrected at the same time. If the default Newton nonlinear solver is used, this amounts to performing a modified Newton iteration on the combined nonlinear system.In the
IDA_STAGGERED
approach, the correction step for the sensitivity variables takes place at the same time for all sensitivity equations, but only after the correction of the state variables has converged and the state variables have passed the local error test.
resS
– is the C function which computes all sensitivity ODE residuals at the same time. For full details seeIDASensResFn
.yS0
– a pointer to an array ofNs
vectors containing the initial values of the sensitivities of \(y\).ypS0
– a pointer to an array ofNs
vectors containing the initial values of the sensitivities of \(\dot{y}\).
 Return value:
IDA_SUCCESS
– The call toIDASensInit()
was successful.IDA_MEM_NULL
– The IDAS memory block was not initialized through a previous call toIDACreate()
.IDA_MEM_FAIL
– A memory allocation request has failed.IDA_ILL_INPUT
– An input argument toIDASensInit()
has an illegal value.
Notes:
Passing
fs == NULL
indicates using the default internal difference quotient sensitivity residual routine andIDASetSensParams()
must be called beforeIDASolve()
.If an error occurred,
IDASensInit()
also sends an error message to the error handler function.
In terms of the problem size \(N\), number of sensitivity vectors
\(N_s\), and maximum method order maxord
, the size of the real workspace
is increased as follows:
Base value: \(\texttt{lenrw} = \texttt{lenrw} + (\texttt{maxord}+5)N_s N\)
With
IDASensSVtolerances()
: \(texttt{lenrw} = \texttt{lenrw} + N_s N\)
the size of the integer workspace is increased as follows:
Base value: \(\texttt{leniw} = \texttt{leniw} + (\texttt{maxord}+5)N_s N_i\)
With
IDASensSVtolerances()
: \(\texttt{leniw} = \texttt{leniw} + N_s N_i\)
where \(N_i\) is the number of integers in one N_Vector
.
The routine IDASensReInit()
, useful during the solution of a sequence of
problems of same size, reinitializes the sensitivityrelated internal memory.
The call to it must follow a call to IDASensInit()
(and maybe a call to
IDAReInit()
). The number Ns
of sensitivities is assumed to be
unchanged since the call to the initialization function. The call to the
IDASensReInit()
function has the form:

int IDASensReInit(void *ida_mem, int ism, N_Vector *yS0, N_Vector *ypS0)
The routine
IDASensReInit()
reinitializes forward sensitivity computations. Arguments:
ida_mem
– pointer to the IDAS memory block returned byIDACreate()
.ism
– forward sensitivity analysis!correction strategies a flag used to select the sensitivity solution method. Its value can beIDA_SIMULTANEOUS
,IDA_STAGGERED
, orIDA_STAGGERED1
.yS0
– a pointer to an array ofNs
variables of typeN_Vector
containing the initial values of the sensitivities.ypS0
– a pointer to an array ofNs
variables of typeN_Vector
containing the initial values of the sensitivities of \(\dot{y}\).
 Return value:
IDA_SUCCESS
– The call toIDASensReInit()
was successful.IDA_MEM_NULL
– The IDAS memory block was not initialized through a previous call toIDACreate()
.IDA_NO_SENS
– Memory space for sensitivity integration was not allocated through a previous call toIDASensInit()
.IDA_ILL_INPUT
– An input argument toIDASensReInit()
has an illegal value.IDA_MEM_FAIL
– A memory allocation request has failed.
Notes:
All arguments of
IDASensReInit()
are the same as those of the functionsIDASensInit()
. If an error occurred,IDASensReInit()
also sends a message to the error handler function.
To deallocate all forward sensitivityrelated memory (allocated in a prior call
to IDASensInit()
), the user must call

void IDASensFree(void *ida_mem)
The function
IDASensFree()
frees the memory allocated for forward sensitivity computations by a previous call toIDASensInit()
. Arguments:
ida_mem
– pointer to the IDAS memory block returned byIDACreate()
.
 Return value:
The function has no return value.
 Notes:
In general,
IDASensFree()
need not be called by the user, as it is invoked automatically byIDAFree()
.After a call to
IDASensFree()
, forward sensitivity computations can be reactivated only by callingIDASensInit()
.
To activate and deactivate forward sensitivity calculations for successive IDAS runs, without having to allocate and deallocate memory, the following function is provided:

int IDASensToggleOff(void *ida_mem)
The function
IDASensToggleOff()
deactivates forward sensitivity calculations. It does not deallocate sensitivityrelated memory. Arguments:
ida_mem
– pointer to the memory previously returned byIDACreate()
.
 Return value:
IDA_SUCCESS
–IDASensToggleOff()
was successful.IDA_MEM_NULL
–ida_mem
wasNULL
.
 Notes:
Since sensitivityrelated memory is not deallocated, sensitivities can be reactivated at a later time (using
IDASensReInit()
).
6.4.4.2.2. Forward sensitivity tolerance specification functions
One of the following three functions must be called to specify the
integration tolerances for sensitivities. Note that this call must be made after
the call to IDASensInit()
.

int IDASensSStolerances(void *ida_mem, sunrealtype reltolS, sunrealtype *abstolS)
The function
IDASensSStolerances()
specifies scalar relative and absolute tolerances. Arguments:
ida_mem
– pointer to the IDAS memory block returned byIDACreate()
.reltolS
– is the scalar relative error tolerance.abstolS
– is a pointer to an array of lengthNs
containing the scalar absolute error tolerances, one for each parameter.
 Return value:
IDA_SUCCESS
– The call toIDASStolerances()
was successful.IDA_MEM_NULL
– The IDAS memory block was not initialized through a previous call toIDACreate()
.IDA_NO_SENS
– The sensitivity allocation functionIDASensInit()
has not been called.IDA_ILL_INPUT
– One of the input tolerances was negative.

int IDASensSVtolerances(void *ida_mem, sunrealtype reltolS, N_Vector *abstolS)
The function
IDASensSVtolerances()
specifies scalar relative tolerance and vector absolute tolerances. Arguments:
ida_mem
– pointer to the IDAS memory block returned byIDACreate()
.reltolS
– is the scalar relative error tolerance.abstolS
– is an array ofNs
variables of typeN_Vector
. TheN_Vector
fromabstolS[is]
specifies the vector tolerances foris
th sensitivity.
 Return value:
IDA_SUCCESS
– The call toIDASVtolerances()
was successful.IDA_MEM_NULL
– The IDAS memory block was not initialized through a previous call toIDACreate()
.IDA_NO_SENS
– The allocation function for sensitivities has not been called.IDA_ILL_INPUT
– The relative error tolerance was negative or an absolute tolerance vector had a negative component.
 Notes:
This choice of tolerances is important when the absolute error tolerance needs to be different for each component of any vector
yS[i]
.

int IDASensEEtolerances(void *ida_mem)
When
IDASensEEtolerances()
is called, IDAS will estimate tolerances for sensitivity variables based on the tolerances supplied for states variables and the scaling factors \(\bar p\). Arguments:
ida_mem
– pointer to the IDAS memory block returned byIDACreate()
.
 Return value:
IDA_SUCCESS
– The call toIDASensEEtolerances()
was successful.IDA_MEM_NULL
– The IDAS memory block was not initialized through a previous call toIDACreate()
.IDA_NO_SENS
– The sensitivity allocation function has not been called.
6.4.4.2.3. Forward sensitivity nonlinear solver interface functions
As in the pure DAE case, when computing solution sensitivities using forward
sensitivitiy analysis IDAS uses the SUNNonlinearSolver
implementation of
Newton’s method defined by the SUNNONLINSOL_NEWTON
module (see
§11.7) by default. To specify a different nonlinear
solver in IDAS, the user’s program must create a SUNNonlinearSolver
object
by calling the appropriate constructor routine. The user must then attach the
SUNNonlinearSolver
object to IDAS by calling
IDASetNonlinearSolverSensSim()
when using the IDA_SIMULTANEOUS
corrector option, or IDASetNonlinearSolver()
and
IDASetNonlinearSolverSensStg()
when using the IDA_STAGGERED
corrector option as documented below.
When changing the nonlinear solver in IDAS, IDASetNonlinearSolver()
must
be called after IDAInit()
; similarly
IDASetNonlinearSolverSensSim()
, IDASetNonlinearSolverSensStg()
, must
be called after IDASensInit()
. If any calls to IDASolve()
have been
made, then IDAS will need to be reinitialized by calling IDAReInit()
to
ensure that the nonlinear solver is initialized correctly before any subsequent
calls to IDASolve()
.
The first argument passed to the routines
IDASetNonlinearSolverSensSim()
, and
IDASetNonlinearSolverSensStg()
, is the IDAS memory pointer returned by
IDACreate()
and the second argument is the SUNNonlinearSolver
object
to use for solving the nonlinear systems (6.4). A call to this function
attaches the nonlinear solver to the main IDAS integrator.

int IDASetNonlinearSolverSensSim(void *ida_mem, SUNNonlinearSolver NLS)
The function
IDASetNonlinearSolverSensSim()
attaches aSUNNonlinearSolver
object (NLS
) to IDAS when using theIDA_SIMULTANEOUS
approach to correct the state and sensitivity variables at the same time. Arguments:
ida_mem
– pointer to the IDAS memory block.NLS
–SUNNonlinearSolver
object to use for solving nonlinear system (6.4).
 Return value:
IDA_SUCCESS
– The nonlinear solver was successfully attached.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_ILL_INPUT
– The SUNNONLINSOL object isNULL
, does not implement the required nonlinear solver operations, is not of the correct type, or the residual function, convergence test function, or maximum number of nonlinear iterations could not be set.

int IDASetNonlinearSolverSensStg(void *ida_mem, SUNNonlinearSolver NLS)
The function
IDASetNonlinearSolverSensStg()
attaches aSUNNonlinearSolver
object (NLS
) to IDAS when using theIDA_STAGGERED
approach to correct all the sensitivity variables after the correction of the state variables. Arguments:
ida_mem
– pointer to the IDAS memory block.NLS
– SUNNONLINSOL object to use for solving nonlinear systems.
 Return value:
IDA_SUCCESS
– The nonlinear solver was successfully attached.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_ILL_INPUT
– The SUNNONLINSOL object isNULL
, does not implement the required nonlinear solver operations, is not of the correct type, or the residual function, convergence test function, or maximum number of nonlinear iterations could not be set.
 Notes:
This function only attaches the
SUNNonlinearSolver
object for correcting the sensitivity variables. To attach aSUNNonlinearSolver
object for the state variable correction useIDASetNonlinearSolver()
.
6.4.4.2.4. Forward sensitivity initial condition calculation function
IDACalcIC()
also calculates corrected initial conditions for sensitivity
variables of a DAE system. When used for initial conditions calculation of the
forward sensitivities, IDACalcIC()
must be preceded by successful calls
to IDASensInit()
(or IDASensReInit()
) and should precede the
call(s) to IDASolve()
. For restrictions that apply for initial
conditions calculation of the state variables, see
§6.4.1.3.7.
Calling IDACalcIC()
is optional. It is only necessary when the initial
conditions do not satisfy the sensitivity systems. Even if forward sensitivity
analysis was enabled, the call to the initial conditions calculation function
IDACalcIC()
is exactly the same as for state variables.
flag = IDACalcIC(ida_mem, icopt, tout1);
See IDACalcIC()
for a list of possible return values.
6.4.4.2.5. IDAS solver function
Even if forward sensitivity analysis was enabled, the call to the main solver
function IDASolve()
is exactly the same as in §6.4.1.
However, in this case the return value flag
can also be one of the
following:
IDA_SRES_FAIL
– The sensitivity residual function failed in an unrecoverable manner.IDA_REP_SRES_ERR
– The user’s residual function repeatedly returned a recoverable error flag, but the solver was unable to recover.
6.4.4.2.6. Forward sensitivity extraction functions
If forward sensitivity computations have been initialized by a call to
IDASensInit()
, or reinitialized by a call to IDASensReInit()
,
then IDAS computes both a solution and sensitivities at time t
. However,
IDASolve()
will still return only the solution \(y\) in yout
.
Solution sensitivities can be obtained through one of the following functions:

int IDAGetSens(void *ida_mem, sunrealtype *tret, N_Vector *yS)
The function
IDAGetSens()
returns the sensitivity solution vectors after a successful return fromIDASolve()
. Arguments:
ida_mem
– pointer to the memory previously allocated byIDAInit()
.tret
– the time reached by the solver output.yS
– array of computed forward sensitivity vectors. This vector array must be allocated by the user.
 Return value:
IDA_SUCCESS
–IDAGetSens()
was successful.IDA_MEM_NULL
–ida_mem
wasNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.IDA_BAD_DKY
–yS
isNULL
.
 Notes:
Note that the argument
tret
is an output for this function. Its value will be the same as that returned at the lastIDASolve()
call.
The function IDAGetSensDky()
computes the k
th derivatives of the
interpolating polynomials for the sensitivity variables at time t
. This
function is called by IDAGetSens()
with k
\(= 0\), but may also be
called directly by the user.

int IDAGetSensDky(void *ida_mem, sunrealtype t, int k, N_Vector *dkyS)
The function
IDAGetSensDky()
returns derivatives of the sensitivity solution vectors after a successful return fromIDASolve()
. Arguments:
ida_mem
– pointer to the memory previously allocated byIDAInit()
.t
– specifies the time at which sensitivity information is requested. The timet
must fall within the interval defined by the last successful step taken by IDAS.k
– order of derivatives.k
must be in the range \(0, 1, ..., klast\) where \(klast\) is the method order of the last successful step.dkyS
– array ofNs
vectors containing the derivatives on output. The space fordkyS
must be allocated by the user.
 Return value:
IDA_SUCCESS
–IDAGetSensDky()
succeeded.IDA_MEM_NULL
–ida_mem
wasNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.IDA_BAD_DKY
– One of the vectorsdkyS[i]
isNULL
.IDA_BAD_K
–k
is not in the range \(0, 1, ...,\)qlast
.IDA_BAD_T
– The timet
is not in the allowed range.
Forward sensitivity solution vectors can also be extracted separately for each
parameter in turn through the functions IDAGetSens1()
and
IDAGetSensDky1()
, defined as follows:

int IDAGetSens1(void *ida_mem, sunrealtype *tret, int is, N_Vector yS)
The function
IDAGetSens1
returns theis
th sensitivity solution vector after a successful return fromIDASolve()
. Arguments:
ida_mem
– pointer to the memory previously allocated byIDAInit()
.tret
– the time reached by the solver output.is
– specifies which sensitivity vector is to be returned \(0\le\)is
\(< N_s\).yS
– the computed forward sensitivity vector. This vector array must be allocated by the user.
 Return value:
IDA_SUCCESS
–IDAGetSens1
was successful.IDA_MEM_NULL
–ida_mem
wasNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.IDA_BAD_IS
– The indexis
is not in the allowed range.IDA_BAD_DKY
–yS
isNULL
.IDA_BAD_T
– The timet
is not in the allowed range.
 Notes:
Note that the argument
tret
is an output for this function. Its value will be the same as that returned at the lastIDASolve()
call.

int IDAGetSensDky1(void *ida_mem, sunrealtype t, int k, int is, N_Vector dkyS)
The function
IDAGetSensDky1
returns thek
th derivative of theis
th sensitivity solution vector after a successful return fromIDASolve()
. Arguments:
ida_mem
– pointer to the memory previously allocated byIDAInit()
.t
– specifies the time at which sensitivity information is requested. The timet
must fall within the interval defined by the last successful step taken by IDAS.k
– order of derivative.is
– specifies the sensitivity derivative vector to be returned \(0\le\)is
\(< N_s\).dkyS
– the vector containing the derivative. The space fordkyS
must be allocated by the user.
 Return value:
IDA_SUCCESS
–IDAGetQuadDky1
succeeded.IDA_MEM_NULL
– The pointer toida_mem
wasNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.IDA_BAD_DKY
–dkyS
or one of the vectorsdkyS[i]
isNULL
.IDA_BAD_IS
– The indexis
is not in the allowed range.IDA_BAD_K
–k
is not in the range \(0, 1, ...,\)qlast
.IDA_BAD_T
– The timet
is not in the allowed range.
6.4.4.2.7. Optional inputs for forward sensitivity analysis
Optional input variables that control the computation of sensitivities can be
changed from their default values through calls to IDASetSens*
functions.
Table 6.8 lists all forward
sensitivity optional input functions in IDAS which are described in detail in
the remainder of this section.
We note that, on an error return, all of the optional input functions send an
error message to the error handler function. All error return values are
negative, so the test flag < 0
will catch all errors. Finally, a call to a
IDASetSens***
function can be made from the user’s calling program at any
time and, if successful, takes effect immediately.
Optional input 
Routine name 
Default 

Sensitivity scaling factors 


DQ approximation method 
centered/0.0 

Error control strategy 


Maximum no. of nonlinear iterations 
4 

int IDASetSensParams(void *ida_mem, sunrealtype *p, sunrealtype *pbar, int *plist)
The function
IDASetSensParams()
specifies problem parameter information for sensitivity calculations. Arguments:
ida_mem
– pointer to the IDAS memory block.p
– a pointer to the array of real problem parameters used to evaluate \(F(t,y,\dot{y},p)\). If nonNULL
,p
must point to a field in the user’s data structureuser_data
passed to the residual function.pbar
– an array ofNs
positive scaling factors. If nonNULL
,pbar
must have all its components \(> 0.0\).plist
– an array ofNs
nonnegative indices to specify which componentsp[i]
to use in estimating the sensitivity equations. If nonNULL
,plist
must have all components \(\ge 0\).
 Return value:
IDA_SUCCESS
– The optional value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.IDA_ILL_INPUT
– An argument has an illegal value.
Note
The array
p
only needs to include the parameters with respect to which sensitivities are (potentially) desired.If the user provides a function to evaluate the sensitivity residuals,
p
need not be specified.When computing the sensitivity residual via a differencequotient or estimating sensitivity tolerances the results will be more accurate if order of magnitude information is provided with
pbar
. Typically, ifp[0] != 0
, the valuepbar[i] = abs(p[plist[i]])
can be used. By default IDAS usesp[i] = 1.0
.If the user provides a function to evaluate the sensitivity residual and specifies tolerances for the sensitivity variables,
pbar
need not be specified.By default IDA computes sensitivities with respect to the first
Ns
parameters inp
i.e.,plist[i] = i
fori = 0,...,Ns1
. If sensitivities with respect to the \(j\)th parameterp[j]
are desired, setplist[i] = j
for some \(0 \leq i < N_s\) and \(0 \leq j < N_p\) where \(N_p\) is the number of element inp
.If the user provides a function to evaluate the sensitivity residuals,
plist
need not be specified.Warning
This function must be preceded by a call to
IDASensInit()
.The array
p
must also be attached to the user data structure. For example,user_data>p = p;
.

int IDASetSensDQMethod(void *ida_mem, int DQtype, sunrealtype DQrhomax)
The function
IDASetSensDQMethod()
specifies the difference quotient strategy in the case in which the residual of the sensitivity equations are to be computed by IDAS. Arguments:
ida_mem
– pointer to the IDAS memory block.DQtype
– specifies the difference quotient type. Its value can beIDA_CENTERED
orIDA_FORWARD
.DQrhomax
– positive value of the selection parameter used in deciding switching between a simultaneous or separate approximation of the two terms in the sensitivity residual.
 Return value:
IDA_SUCCESS
– The optional value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_ILL_INPUT
– An argument has an illegal value.
Notes:
If
DQrhomax
\(= 0.0\), then no switching is performed. The approximation is done simultaneously using either centered or forward finite differences, depending on the value ofDQtype
. For values ofDQrhomax
\(\ge 1.0\), the simultaneous approximation is used whenever the estimated finite difference perturbations for states and parameters are within a factor ofDQrhomax
, and the separate approximation is used otherwise. Note that a valueDQrhomax
\(<1.0\) will effectively disable switching. See §6.2.6 for more details.The default value are
DQtype == IDA_CENTERED
andDQrhomax
\(=0.0\).

int IDASetSensErrCon(void *ida_mem, sunbooleantype errconS)
The function
IDASetSensErrCon()
specifies the error control strategy for sensitivity variables. Arguments:
ida_mem
– pointer to the IDAS memory block.errconS
– specifies whether sensitivity variables are to be includedSUNTRUE
or notSUNFALSE
in the error control mechanism.
 Return value:
IDA_SUCCESS
– The optional value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.
 Notes:
By default,
errconS
is set toSUNFALSE
. IferrconS = SUNTRUE
then both state variables and sensitivity variables are included in the error tests. IferrconS = SUNFALSE
then the sensitivity variables are excluded from the error tests. Note that, in any event, all variables are considered in the convergence tests.

int IDASetSensMaxNonlinIters(void *ida_mem, int maxcorS)
The function
IDASetSensMaxNonlinIters()
specifies the maximum number of nonlinear solver iterations for sensitivity variables per step. Arguments:
ida_mem
– pointer to the IDAS memory block.maxcorS
– maximum number of nonlinear solver iterations allowed per step \(> 0\).
 Return value:
IDA_SUCCESS
– The optional value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_MEM_FAIL
– The SUNNONLINSOL module isNULL
.
 Notes:
The default value is 3.
6.4.4.2.8. Optional outputs for forward sensitivity analysis
Optional output functions that return statistics and solver performance information related to forward sensitivity computations are listed in Table 6.9 and described in detail in the remainder of this section.
Optional output 
Routine name 

No. of calls to sensitivity residual function 

No. of calls to residual function for sensitivity 

No. of sensitivity local error test failures 

No. of failed steps due to sensitivity nonlinear solver failures 

No. of calls to lin. solv. setup routine for sens. 

Error weight vector for sensitivity variables 

Sensitivityrelated statistics as a group 

No. of sens. nonlinear solver iterations 

No. of sens. convergence failures 

Sens. nonlinear solver statistics as a group 

int IDAGetSensNumResEvals(void *ida_mem, long int *nfSevals)
The function
IDAGetSensNumResEvals()
returns the number of calls to the sensitivity residual function. Arguments:
ida_mem
– pointer to the IDAS memory block.nfSevals
– number of calls to the sensitivity residual function.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.

int IDAGetNumResEvalsSens(void *ida_mem, long int *nfevalsS)
The function
IDAGetNumResEvalsSens()
returns the number of calls to the user’s residual function due to the internal finite difference approximation of the sensitivity residuals. Arguments:
ida_mem
– pointer to the IDAS memory block.nfevalsS
– number of calls to the user’s DAE residual function for the evaluation of sensitivity residuals.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.
 Notes:
This counter is incremented only if the internal finite difference approximation routines are used for the evaluation of the sensitivity residuals.

int IDAGetSensNumErrTestFails(void *ida_mem, long int *nSetfails)
The function
IDAGetSensNumErrTestFails()
returns the number of local error test failures for the sensitivity variables that have occurred. Arguments:
ida_mem
– pointer to the IDAS memory block.nSetfails
– number of error test failures.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.
 Notes:
This counter is incremented only if the sensitivity variables have been included in the error test (see
IDASetSensErrCon()
). Even in that case, this counter is not incremented if theism = IDA_SIMULTANEOUS
sensitivity solution method has been used.

int IDAGetNumStepSensSolveFails(void *ida_mem, long int *nSncfails)
Returns the number of failed steps due to a sensitivity nonlinear solver failure.
 Arguments:
ida_mem
– pointer to the IDAS memory block.nSncfails
– number of step failures.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.

int IDAGetSensNumLinSolvSetups(void *ida_mem, long int *nlinsetupsS)
The function
IDAGetSensNumLinSolvSetups()
returns the number of calls to the linear solver setup function due to forward sensitivity calculations. Arguments:
ida_mem
– pointer to the IDAS memory block.nlinsetupsS
– number of calls to the linear solver setup function.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.
 Notes:
This counter is incremented only if a nonlinear solver requiring a linear solve has been used and the
ism = IDA_STAGGERED
sensitivity solution method has been specified (see §6.4.4.2.1).

int IDAGetSensStats(void *ida_mem, long int *nresSevals, long int *nresevalsS, long int *nSetfails, long int *nlinsetupsS)
The function
IDAGetSensStats()
returns all of the above sensitivityrelated solver statistics as a group. Arguments:
ida_mem
– pointer to the IDAS memory block.nresSevals
– number of calls to the sensitivity residual function.nresevalsS
– number of calls to the usersupplied DAE residual function for sensitivity evaluations.nSetfails
– number of error test failures.nlinsetupsS
– number of calls to the linear solver setup function.
 Return value:
IDA_SUCCESS
– The optional output values have been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.

int IDAGetSensErrWeights(void *ida_mem, N_Vector *eSweight)
The function
IDAGetSensErrWeights()
returns the sensitivity error weight vectors at the current time. These are the reciprocals of the \(W_i\) of (6.5) for the sensitivity variables. Arguments:
ida_mem
– pointer to the IDAS memory block.eSweight
– pointer to the array of error weight vectors.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.
 Notes:
The user must allocate memory for
eweightS
.

int IDAGetSensNumNonlinSolvIters(void *ida_mem, long int *nSniters)
The function
IDAGetSensNumNonlinSolvIters()
returns the number of nonlinear iterations performed for sensitivity calculations. Arguments:
ida_mem
– pointer to the IDAS memory block.nSniters
– number of nonlinear iterations performed.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.IDA_MEM_FAIL
– The SUNNONLINSOL module isNULL
.
 Notes:
This counter is incremented only if
ism
wasIDA_STAGGERED
or in the call toIDASensInit()
.

int IDAGetSensNumNonlinSolvConvFails(void *ida_mem, long int *nSncfails)
The function
IDAGetSensNumNonlinSolvConvFails()
returns the number of nonlinear convergence failures that have occurred for sensitivity calculations. Arguments:
ida_mem
– pointer to the IDAS memory block.nSncfails
– number of nonlinear convergence failures.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.
 Notes:
This counter is incremented only if
ism
wasIDA_STAGGERED
or in the call toIDASensInit()
.

int IDAGetSensNonlinSolvStats(void *ida_mem, long int *nSniters, long int *nSncfails)
The function
IDAGetSensNonlinSolvStats()
returns the sensitivityrelated nonlinear solver statistics as a group. Arguments:
ida_mem
– pointer to the IDAS memory block.nSniters
– number of nonlinear iterations performed.nSncfails
– number of nonlinear convergence failures.
 Return value:
IDA_SUCCESS
– The optional output values have been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.IDA_MEM_FAIL
– The SUNNONLINSOL module isNULL
.
6.4.4.2.9. Initial condition calculation optional output functions
The sensitivity consistent initial conditions found by IDAS (after a successful
call to IDACalcIC()
) can be obtained by calling the following function:

int IDAGetSensConsistentIC(void *ida_mem, N_Vector *yyS0_mod, N_Vector *ypS0_mod)
The function
IDAGetSensConsistentIC()
returns the corrected initial conditions calculated byIDACalcIC()
for sensitivities variables. Arguments:
ida_mem
– pointer to the IDAS memory block.yyS0_mod
– a pointer to an array ofNs
vectors containing consistent sensitivity vectors.ypS0_mod
– a pointer to an array ofNs
vectors containing consistent sensitivity derivative vectors.
 Return value:
IDA_SUCCESS
–IDAGetSensConsistentIC()
succeeded.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– The functionIDASensInit()
has not been previously called.IDA_ILL_INPUT
–IDASolve()
has been already called.
 Notes:
If the consistent sensitivity vectors or consistent derivative vectors are not desired, pass
NULL
for the corresponding argument.Warning
The user must allocate space for
yyS0_mod
andypS0_mod
(if notNULL
).
6.4.4.3. Usersupplied routines for forward sensitivity analysis
In addition to the required and optional usersupplied routines described in §6.4.1.4, when using IDAS for forward sensitivity analysis, the user has the option of providing a routine that calculates the residual of the sensitivity equations (6.11).
By default, IDAS uses difference quotient approximation routines for the residual of the sensitivity equations. However, IDAS allows the option for userdefined sensitivity residual routines (which also provides a mechanism for interfacing IDAS to routines generated by automatic differentiation).
The user may provide the residuals of the sensitivity equations (6.11)
for all sensitivity parameters at once, through a function of type
IDASensResFn
defined by:

typedef int (*IDASensResFn)(int Ns, sunrealtype t, N_Vector yy, N_Vector yp, N_Vector resval, N_Vector *yS, N_Vector *ypS, N_Vector *resvalS, void *user_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3)
This function computes the sensitivity residual for all sensitivity equations. It must compute the vectors \(\left({\partial F}/{\partial y_i}\right)s_i(t) + \left({\partial F}/{\partial \dot y}\right) \dot{s}_i(t) + \left({\partial F}/{\partial p_i}\right)\) and store them in
resvalS[i]
. Arguments:
Ns
– is the number of sensitivities.t
– is the current value of the independent variable.yy
– is the current value of the state vector, \(y(t)\) .yp
– is the current value of \(\dot{y}(t)\) .resval
– contains the current value \(F\) of the original DAE residual.yS
– contains the current values of the sensitivities \(s_i\) .ypS
– contains the current values of the sensitivity derivatives \(\dot{s}_i\) .resvalS
– contains the output sensitivity residual vectors. Memory allocation forresvalS
is handled within IDAS.user_data
– is a pointer to user data.tmp1
,tmp2
,tmp3
– areN_Vector
s of length \(N\) which can be used as temporary storage.
 Return value:
An
IDASensResFn()
should return 0 if successful, a positive value if a recoverable error occurred (in which case IDAS will attempt to correct), or a negative value if it failed unrecoverably (in which case the integration is halted andIDA_SRES_FAIL
is returned). Notes:
There is one situation in which recovery is not possible even if
IDASensResFn()
function returns a recoverable error flag. That is when this occurs at the very first call to theIDASensResFn()
, in which case IDAS returnsIDA_FIRST_RES_FAIL
.
6.4.4.4. Integration of quadrature equations depending on forward sensitivities
IDAS provides support for integration of quadrature equations that depends not only on the state variables but also on forward sensitivities.
The following is an overview of the sequence of calls in a user’s main program in this situation. Steps that are unchanged from the skeleton program presented in §6.4.1.2 are grayed out and new or modified steps are in bold. See also §6.4.2.
Initialize parallel or multithreaded environment, if appropriate
Create the SUNDIALS context object
Set vector of initial values
Create matrix object
Create linear solver object
Set linear solver optional inputs
Create nonlinear solver object
Create IDAS object
Initialize IDAS solver
Specify integration tolerances
Attach linear solver
Set linear solver optional inputs
Attach nonlinear solver
Set nonlinear solver optional inputs
Set sensitivity initial values
Activate sensitivity calculations
Set sensitivity integration tolerances
Create sensitivity nonlinear solver
Attach the sensitivity nonlinear solver
Set sensitivity nonlinear solver optional inputs
Set vector of initial values for quadrature variables
Typically, the quadrature variables should be initialized to \(0\).
Initialize sensitivitydependent quadrature integration
Call
IDAQuadSensInit()
to specify the quadrature equation righthand side function and to allocate internal memory related to quadrature integration.Specify rootfinding problem
Set optional inputs
Call
IDASetQuadSensErrCon()
to indicate whether or not quadrature variables should be used in the step size control mechanism. If so, one of theIDAQuadSens*tolerances
functions must be called to specify the integration tolerances for quadrature variables. See §6.4.2.4 for details.Correct initial values
Advance solution in time
Extract sensitivity solution
Extract sensitivitydependent quadrature variables
Call
IDAGetQuadSens()
,IDAGetQuadSens1()
,IDAGetQuadSensDky()
orIDAGetQuadSensDky1()
to obtain the values of the quadrature variables or their derivatives at the current time.Get optional outputs
Call
IDAGetQuadSens*
functions to obtain optional output related to the integration of sensitivitydependent quadratures. See §6.4.4.4.5 for details.Destroy objects
Finalize MPI, if used
6.4.4.4.1. Sensitivitydependent quadrature initialization and deallocation
The function IDAQuadSensInit()
activates integration of quadrature equations
depending on sensitivities and allocates internal memory related to these
calculations. If rhsQS
is input as NULL
, then IDAS uses an internal
function that computes difference quotient approximations to the functions
\(\bar q_i = (\partial q / \partial y) s_i + (\partial q / \partial \dot{y}) \dot{s}_i + \partial q / \partial p_i\),
in the notation of (6.10). The form of the call to this function is as follows:

int IDAQuadSensInit(void *ida_mem, IDAQuadSensRhsFn rhsQS, N_Vector *yQS0)
The function
IDAQuadSensInit()
provides required problem specifications, allocates internal memory, and initializes quadrature integration. Arguments:
ida_mem
– pointer to the IDAS memory block returned byIDACreate()
.rhsQS
– is theIDAQuadSensRhsFn
function which computes \(f_{QS}\) , the righthand side of the sensitivitydependent quadrature equations.yQS0
– contains the initial values of sensitivitydependent quadratures.
 Return value:
IDA_SUCCESS
– The call toIDAQuadSensInit()
was successful.IDA_MEM_NULL
– The IDAS memory was not initialized by a prior call toIDACreate()
.IDA_MEM_FAIL
– A memory allocation request failed.IDA_NO_SENS
– The sensitivities were not initialized by a prior call toIDASensInit()
.IDA_ILL_INPUT
– The parameteryQS0
isNULL
.
 Notes:
Warning
Before calling
IDAQuadSensInit()
, the user must enable the sensitivites by callingIDASensInit()
. If an error occurred,IDAQuadSensInit()
also sends an error message to the error handler function.
In terms of the number of quadrature variables \(N_q\) and maximum method
order maxord
, the size of the real workspace is increased as follows:
Base value: \(\text{\texttt{lenrw}} = \text{\texttt{lenrw}} + (\text{\texttt{maxord}} + 5) N_q\)
If
IDAQuadSensSVtolerances()
is called: \(\text{\texttt{lenrw}} = \text{\texttt{lenrw}} + N_q N_s\)
and the size of the integer workspace is increased as follows:
Base value: \(\text{\texttt{leniw}} = \text{\texttt{leniw}} + (\text{\texttt{maxord}} + 5) N_q\)
If
IDAQuadSensSVtolerances()
is called: \(\text{\texttt{leniw}} = \text{\texttt{leniw}} + N_q N_s\)
The function IDAQuadSensReInit()
, useful during the solution of a sequence of
problems of same size, reinitializes the quadrature related internal memory and
must follow a call to IDAQuadSensInit()
. The number Nq
of quadratures as
well as the number Ns
of sensitivities are assumed to be unchanged from the
prior call to IDAQuadSensInit()
. The call to the IDAQuadSensReInit()
function has the form:

int IDAQuadSensReInit(void *ida_mem, N_Vector *yQS0)
The function
IDAQuadSensReInit()
provides required problem specifications and reinitializes the sensitivitydependent quadrature integration. Arguments:
ida_mem
– pointer to the IDAS memory block.yQS0
– contains the initial values of sensitivitydependent quadratures.
 Return value:
IDA_SUCCESS
– The call toIDAQuadSensReInit()
was successful.IDA_MEM_NULL
– The IDAS memory was not initialized by a prior call toIDACreate()
.IDA_NO_SENS
– Memory space for the sensitivity calculation was not allocated by a prior call toIDASensInit()
.IDA_NO_QUADSENS
– Memory space for the sensitivity quadratures integration was not allocated by a prior call toIDAQuadSensInit()
.IDA_ILL_INPUT
– The parameteryQS0
isNULL
.
 Notes:
If an error occurred,
IDAQuadSensReInit()
also sends an error message to the error handler function.

void IDAQuadSensFree(void *ida_mem);
The function
IDAQuadSensFree()
frees the memory allocated for sensitivity quadrature integration. Arguments:
ida_mem
– pointer to the IDAS memory block.
 Return value:
There is no return value.
 Notes:
In general,
IDAQuadSensFree()
need not be called by the user as it is called automatically byIDAFree()
.
6.4.4.4.2. IDAS solver function
Even if quadrature integration was enabled, the call to the main solver function
IDASolve()
is exactly the same as in §6.4.1.
However, in this case the return value flag
can also be one of the
following:
IDA_QSRHS_FAIL
– the sensitivity quadrature righthand side function failed in an unrecoverable manner.IDA_FIRST_QSRHS_ERR
– the sensitivity quadrature righthand side function failed at the first call.IDA_REP_QSRHS_ERR
– convergence test failures occurred too many times due to repeated recoverable errors in the quadrature righthand side function. TheIDA_REP_RES_ERR
will also be returned if the quadrature righthand side function had repeated recoverable errors during the estimation of an initial step size (assuming the sensitivity quadrature variables are included in the error tests).
6.4.4.4.3. Sensitivitydependent quadrature extraction functions
If sensitivitydependent quadratures have been initialized by a call to IDAQuadSensInit()
, or reinitialized by a call
to IDAQuadSensReInit()
, then IDAS computes a solution, sensitivities, and quadratures depending on sensitivities
at time t
. However, IDASolve()
will still return only the solutions \(y\) and \(\dot{y}\).
Sensitivitydependent quadratures can be obtained using one of the following
functions:

int IDAGetQuadSens(void *ida_mem, sunrealtype *tret, N_Vector *yQS)
The function
IDAGetQuadSens()
returns the quadrature sensitivity solution vectors after a successful return fromIDASolve()
. Arguments:
ida_mem
– pointer to the memory previously allocated byIDAInit()
.tret
– the time reached by the solver output.yQS
– array ofNs
computed sensitivitydependent quadrature vectors. This array of vectors must be allocated by the user.
 Return value:
IDA_SUCCESS
–IDAGetQuadSens()
was successful.IDA_MEM_NULL
–ida_mem
was NULL.IDA_NO_SENS
– Sensitivities were not activated.IDA_NO_QUADSENS
– Quadratures depending on the sensitivities were not activated.IDA_BAD_DKY
–yQS
or one of theyQS[i]
isNULL
.
The function IDAGetQuadSensDky()
computes the k
th derivatives of
the interpolating polynomials for the sensitivitydependent quadrature variables
at time t
. This function is called by IDAGetQuadSens()
with
k = 0
, but may also be called directly by the user.

int IDAGetQuadSensDky(void *ida_mem, sunrealtype t, int k, N_Vector *dkyQS)
The function
IDAGetQuadSensDky()
returns derivatives of the quadrature sensitivities solution vectors after a successful return fromIDASolve()
. Arguments:
ida_mem
– pointer to the memory previously allocated byIDAInit()
.t
– the time at which information is requested. The timet
must fall within the interval defined by the last successful step taken by IDAS.k
– order of the requested derivative.k
must be in the range \(0, 1, ..., klast\) where \(klast\) is the method order of the last successful step.dkyQS
– array ofNs
vectors containing the derivatives. This vector array must be allocated by the user.
 Return value:
IDA_SUCCESS
–IDAGetQuadSensDky()
succeeded.IDA_MEM_NULL
–ida_mem
wasNULL
.IDA_NO_SENS
– Sensitivities were not activated.IDA_NO_QUADSENS
– Quadratures depending on the sensitivities were not activated.IDA_BAD_DKY
–dkyQS
or one of the vectorsdkyQS[i]
isNULL
.IDA_BAD_K
–k
is not in the range \(0, 1, ..., klast\).IDA_BAD_T
– The timet
is not in the allowed range.
Quadrature sensitivity solution vectors can also be extracted separately for
each parameter in turn through the functions IDAGetQuadSens1
and
IDAGetQuadSensDky1
, defined as follows:

int IDAGetQuadSens1(void *ida_mem, sunrealtype *tret, int is, N_Vector yQS)
The function
IDAGetQuadSens1
returns theis
th sensitivity of quadratures after a successful return fromIDASolve()
. Arguments:
ida_mem
– pointer to the memory previously allocated byIDAInit()
.tret
– the time reached by the solver output.is
– specifies which sensitivity vector is to be returned \(0\le\)is
\(< N_s\).yQS
– the computed sensitivitydependent quadrature vector. This vector must be allocated by the user.
 Return value:
IDA_SUCCESS
–IDAGetQuadSens1
was successful.IDA_MEM_NULL
–ida_mem
wasNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.IDA_NO_QUADSENS
– Quadratures depending on the sensitivities were not activated.IDA_BAD_IS
– The indexis
is not in the allowed range.IDA_BAD_DKY
–yQS
isNULL
.

int IDAGetQuadSensDky1(void *ida_mem, sunrealtype t, int k, int is, N_Vector dkyQS)
The function
IDAGetQuadSensDky1
returns thek
th derivative of theis
th sensitivity solution vector after a successful return fromIDASolve()
. Arguments:
ida_mem
– pointer to the memory previously allocated byIDAInit()
.t
– specifies the time at which sensitivity information is requested. The timet
must fall within the interval defined by the last successful step taken by IDAS.k
– order of derivative.k
must be in the range \(0, 1, ..., klast\) where \(klast\) is the method order of the last successful step.is
– specifies the sensitivity derivative vector to be returned \(0\le\)is
\(< N_s\).dkyQS
– the vector containing the derivative. The space fordkyQS
must be allocated by the user.
 Return value:
IDA_SUCCESS
–IDAGetQuadDky1
succeeded.IDA_MEM_NULL
–ida_mem
wasNULL
.IDA_NO_SENS
– Forward sensitivity analysis was not initialized.IDA_NO_QUADSENS
– Quadratures depending on the sensitivities were not activated.IDA_BAD_DKY
–dkyQS
isNULL
.IDA_BAD_IS
– The indexis
is not in the allowed range.IDA_BAD_K
–k
is not in the range \(0, 1, ..., klast\).IDA_BAD_T
– The timet
is not in the allowed range.
6.4.4.4.4. Optional inputs for sensitivitydependent quadrature integration
IDAS provides the following optional input functions to control the integration of sensitivitydependent quadrature equations.

int IDASetQuadSensErrCon(void *ida_mem, sunbooleantype errconQS)
The function
IDASetQuadSensErrCon()
specifies whether or not the quadrature variables are to be used in the local error control mechanism. If they are, the user must specify the error tolerances for the quadrature variables by callingIDAQuadSensSStolerances()
,IDAQuadSensSVtolerances()
, orIDAQuadSensEEtolerances()
. Arguments:
ida_mem
– pointer to the IDAS memory block.errconQS
– specifies whether sensitivity quadrature variables are includedSUNTRUE
or notSUNFALSE
in the error control mechanism.
 Return value:
IDA_SUCCESS
– The optional value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Sensitivities were not activated.IDA_NO_QUADSENS
– Quadratures depending on the sensitivities were not activated.
 Notes:
By default,
errconQS
is set toSUNFALSE
.Warning
It is illegal to call
IDASetQuadSensErrCon()
before a call toIDAQuadSensInit()
.
If the quadrature variables are part of the step size control mechanism, one of the following functions must be called to specify the integration tolerances for quadrature variables.

int IDAQuadSensSStolerances(void *ida_mem, sunrealtype reltolQS, sunrealtype *abstolQS)
The function
IDAQuadSensSStolerances()
specifies scalar relative and absolute tolerances. Arguments:
ida_mem
– pointer to the IDAS memory block.reltolQS
– tolerances is the scalar relative error tolerance.abstolQS
– is a pointer to an array containing theNs
scalar absolute error tolerances.
 Return value:
IDA_SUCCESS
– The optional value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Sensitivities were not activated.IDA_NO_QUADSENS
– Quadratures depending on the sensitivities were not activated.IDA_ILL_INPUT
– One of the input tolerances was negative.

int IDAQuadSensSVtolerances(void *ida_mem, sunrealtype reltolQS, N_Vector *abstolQS)
The function
IDAQuadSensSVtolerances()
specifies scalar relative and vector absolute tolerances. Arguments:
ida_mem
– pointer to the IDAS memory block.reltolQS
– tolerances is the scalar relative error tolerance.abstolQS
– is an array ofNs
variables of typeN_Vector
. TheN_Vector
fromabstolS[is]
specifies the vector tolerances foris
th quadrature sensitivity.
 Return value:
IDA_SUCCESS
– The optional value has been successfully set.IDA_NO_QUAD
– Quadrature integration was not initialized.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS
– Sensitivities were not activated.IDA_NO_QUADSENS
– Quadratures depending on the sensitivities were not activated.IDA_ILL_INPUT
– One of the input tolerances was negative.

int IDAQuadSensEEtolerances(void *ida_mem)
The function
IDAQuadSensEEtolerances()
specifies that the tolerances for the sensitivitydependent quadratures should be estimated from those provided for the pure quadrature variables. Arguments:
ida_mem
– pointer to the IDAS memory block.
 Return value:
IDA_SUCCESS
– The optional value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_SENS – Sensitivities were not activated.
IDA_NO_QUADSENS
– Quadratures depending on the sensitivities were not activated.
 Notes:
When
IDAQuadSensEEtolerances()
is used, before callingIDASolve()
, integration of pure quadratures must be initialized (see §6.4.2) and tolerances for pure quadratures must be also specified (see §6.4.2.4).
6.4.4.4.5. Optional outputs for sensitivitydependent quadrature integration
IDAS provides the following functions that can be used to obtain solver performance information related to quadrature integration.

int IDAGetQuadSensNumRhsEvals(void *ida_mem, long int *nrhsQSevals)
The function
IDAGetQuadSensNumRhsEvals()
returns the number of calls made to the user’s quadrature righthand side function. Arguments:
ida_mem
– pointer to the IDAS memory block.nrhsQSevals
– number of calls made to the user’srhsQS
function.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_QUADSENS
– Sensitivitydependent quadrature integration has not been initialized.

int IDAGetQuadSensNumErrTestFails(void *ida_mem, long int *nQSetfails)
The function
IDAGetQuadSensNumErrTestFails()
returns the number of local error test failures due to quadrature variables. Arguments:
ida_mem
– pointer to the IDAS memory block.nQSetfails
– number of error test failures due to quadrature variables.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_QUADSENS
– Sensitivitydependent quadrature integration has not been initialized.

int IDAGetQuadSensErrWeights(void *ida_mem, N_Vector *eQSweight)
The function
IDAGetQuadSensErrWeights()
returns the quadrature error weights at the current time. Arguments:
ida_mem
– pointer to the IDAS memory block.eQSweight
– array of quadrature error weight vectors at the current time.
 Return value:
IDA_SUCCESS
– The optional output value has been successfully set.IDA_MEM_NULL
– Theida_mem
pointer isNULL
.IDA_NO_QUADSENS
– Sensitivitydependent quadrature integration has not been initialized.
 Notes:
Warning
The user must allocate memory for
eQSweight
. If quadratures were not included in the error control mechanism (through a call toIDASetQuadSensErrCon()
witherrconQS=SUNTRUE
),IDAGetQuadSensErrWeights()
does not set theeQSweight
vector.

int IDAGetQuadSensStats(void *ida_mem, long int *nrhsQSevals, long int *nQSetfails)
The function
IDAGetQuadSensStats()
returns the IDAS integrator statistics as a group. Arguments:
ida_mem
– pointer to the IDAS memory block.nrhsQSevals
– number of calls to the user’srhsQS
function.nQSetfails
– number of error test failures due to quadrature variables.
 Return value:
IDA_SUCCESS
– the optional output values have been successfully set.IDA_MEM_NULL
– theida_mem
pointer isNULL
.IDA_NO_QUADSENS
– Sensitivitydependent quadrature integration has not been initialized.
6.4.4.4.6. Usersupplied function for sensitivitydependent quadrature integration
For the integration of sensitivitydependent quadrature equations, the user must
provide a function that defines the residual of those quadrature equations. For
the sensitivities of quadratures (6.10) with integrand \(q\), the
appropriate residual functions are given by \(\bar{q}_i = {\partial
q}/{\partial y} s_i + {\partial q}/{\partial \dot{y}} \dot{s}_i + {\partial
q}{\partial p_i}\). This user function must be of type
IDAQuadSensRhsFn
defined as follows:

typedef int (*IDAQuadSensRhsFn)(int Ns, sunrealtype t, N_Vector yy, N_Vector yp, N_Vector *yyS, N_Vector *ypS, N_Vector rrQ, N_Vector *rhsvalQS, void *user_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3)
This function computes the sensitivity quadrature equation righthand side for a given value of the independent variable \(t\) and state vector \(y\).
 Arguments:
Ns
– is the number of sensitivity vectors.t
– is the current value of the independent variable.yy
– is the current value of the dependent variable vector, \(y(t)\).yp
– is the current value of the dependent variable vector, \(\dot{y}(t)\).yyS
– is an array ofNs
variables of typeN_Vector
containing the dependent sensitivity vectors \(s_i\).ypS
– is an array ofNs
variables of typeN_Vector
containing the dependent sensitivity derivatives \(\dot{s}_i\).rrQ
– is the current value of the quadrature righthand side \(q\).rhsvalQS
– contains theNs
output vectors.user_data
– is theuser_data
pointer passed toIDASetUserData()
.tmp1
,tmp2
,tmp3
– areN_Vector
s which can be used as temporary storage.
 Return value:
An
IDAQuadSensRhsFn
should return 0 if successful, a positive value if a recoverable error occurred (in which case IDAS will attempt to correct), or a negative value if it failed unrecoverably (in which case the integration is halted andIDA_QRHS_FAIL
is returned).
Notes:
Allocation of memory for
rhsvalQS
is automatically handled within IDAS.Both
yy
andyp
are of typeN_Vector
and bothyyS
andypS
are pointers to an array containingNs
vectors of typeN_Vector
. It is the user’s responsibility to access the vector data consistently (including the use of the correct accessor macros from eachN_Vector
implementation).There is one situation in which recovery is not possible even if
IDAQuadSensRhsFn
function returns a recoverable error flag. That is when this occurs at the very first call to theIDAQuadSensRhsFn
, in which case IDAS returnsIDA_FIRST_QSRHS_ERR
).
6.4.4.5. Note on using partial error control
For some problems, when sensitivities are excluded from the error control test, the behavior of IDAS may appear at first glance to be erroneous. One would expect that, in such cases, the sensitivity variables would not influence in any way the step size selection.
The short explanation of this behavior is that the step size selection
implemented by the error control mechanism in IDAS is based on the magnitude of
the correction calculated by the nonlinear solver. As mentioned in
§6.4.4.2.1, even with partial error
control selected in the call to IDASensInit()
, the sensitivity variables are
included in the convergence tests of the nonlinear solver.
When using the simultaneous corrector method §6.2.6, the nonlinear system that is solved at each step involves both the state and sensitivity equations. In this case, it is easy to see how the sensitivity variables may affect the convergence rate of the nonlinear solver and therefore the step size selection. The case of the staggered corrector approach is more subtle. The sensitivity variables at a given step are computed only once the solver for the nonlinear state equations has converged. However, if the nonlinear system corresponding to the sensitivity equations has convergence problems, IDAS will attempt to improve the initial guess by reducing the step size in order to provide a better prediction of the sensitivity variables. Moreover, even if there are no convergence failures in the solution of the sensitivity system, IDAS may trigger a call to the linear solver’s setup routine which typically involves reevaluation of Jacobian information (Jacobian approximation in the case of matrixbased linear solvers, or preconditioner data in the case of the Krylov solvers). The new Jacobian information will be used by subsequent calls to the nonlinear solver for the state equations and, in this way, potentially affect the step size selection.
When using the simultaneous corrector method it is not possible to decide whether nonlinear solver convergence failures or calls to the linear solver setup routine have been triggered by convergence problems due to the state or the sensitivity equations. When using one of the staggered corrector methods, however, these situations can be identified by carefully monitoring the diagnostic information provided through optional outputs. If there are no convergence failures in the sensitivity nonlinear solver, and none of the calls to the linear solver setup routine were made by the sensitivity nonlinear solver, then the step size selection is not affected by the sensitivity variables.
Finally, the user must be warned that the effect of appending sensitivity equations to a given system of DAEs on the step size selection (through the mechanisms described above) is problemdependent and can therefore lead to either an increase or decrease of the total number of steps that IDAS takes to complete the simulation. At first glance, one would expect that the impact of the sensitivity variables, if any, would be in the direction of increasing the step size and therefore reducing the total number of steps. The argument for this is that the presence of the sensitivity variables in the convergence test of the nonlinear solver can only lead to additional iterations (and therefore a smaller iteration error), or to additional calls to the linear solver setup routine (and therefore more uptodate Jacobian information), both of which will lead to larger steps being taken by IDAS. However, this is true only locally. Overall, a larger integration step taken at a given time may lead to step size reductions at later times, due to either nonlinear solver convergence failures or error test failures.