Provided by: libdogleg-doc_0.08-3_all 
NAME
libdogleg - A general purpose sparse optimizer to solve data fitting problems, such as sparse bundle
adjustment.
DESCRIPTION
This is a library for solving large-scale nonlinear optimization problems. By employing sparse linear
algebra, it is taylored for problems that have weak coupling between the optimization variables. For
appropriately sparse problems this results in massive performance gains.
The main task of this library is to find the vector p that minimizes
norm2( x )
where x = f(p) is a vector that has higher dimensionality than p. The user passes in a callback function
(of type "dogleg_callback_t") that takes in the vector p and returns the vector x and a matrix of
derivatives J = df/dp. J is a matrix with a row for each element of f and a column for each element of p.
J is a sparse matrix, which results in substantial increases in computational efficiency if most entries
of J are 0. J is stored row-first in the callback routine. libdogleg uses a column-first data
representation so it references the transpose of J (called Jt). J stored row-first is identical to Jt
stored column-first; this is purely a naming choice.
This library implements Powell's dog-leg algorithm to solve the problem. Like the more-widely-known
Levenberg-Marquardt algorithm, Powell's dog-leg algorithm solves a nonlinear optimization problem by
interpolating between a Gauss-Newton step and a gradient descent step. Improvements over LM are
• a more natural representation of the linearity of the operating point (trust region size vs a vague
lambda term).
• significant efficiency gains, since a matrix inversion isn't needed to retry a rejected step
The algorithm is described in many places, originally in
M. Powell. A Hybrid Method for Nonlinear Equations. In P. Rabinowitz, editor, Numerical Methods for
Nonlinear Algebraic Equations, pages 87-144. Gordon and Breach Science, London, 1970.
Various enhancements to Powell's original method are described in the literature; at this time only the
original algorithm is implemented here.
The sparse matrix algebra is handled by the CHOLMOD library, written by Tim Davis. Parts of CHOLMOD are
licensed under the GPL and parts under the LGPL. Only the LGPL pieces are used here, allowing libdogleg
to be licensed under the LGPL as well. Due to this I lose some convenience (all simple sparse matrix
arithmetic in CHOLMOD is GPL-ed) and some performance (the fancier computational methods, such as
supernodal analysis are GPL-ed). For my current applications the performance losses are minor.
FUNCTIONS AND TYPES
Main API
dogleg_optimize
This is the main call to the library. Its declared as
double dogleg_optimize(double* p, unsigned int Nstate,
unsigned int Nmeas, unsigned int NJnnz,
dogleg_callback_t* f, void* cookie,
dogleg_solverContext_t** returnContext);
• p is the initial estimate of the state vector (and holds the final result)
• "Nstate" specifies the number of optimization variables (elements of p)
• "Nmeas" specifies the number of measurements (elements of x). "Nmeas >= Nstate" is a requirement
• "NJnnz" specifies the number of non-zero elements of the jacobian matrix df/dp. In a dense matrix
"Jnnz = Nstate*Nmeas". We are dealing with sparse jacobians, so "NJnnz" should be far less. If not,
libdogleg is not an appropriate routine to solve this problem.
• "f" specifies the callback function that the optimization routine calls to sample the problem being
solved
• "cookie" is an arbitrary data pointer passed untouched to "f"
• If not "NULL", "returnContext" can be used to retrieve the full context structure from the solver.
This can be useful since this structure contains the latest operating point values. It also has an
active "cholmod_common" structure, which can be reused if more CHOLMOD routines need to be called
externally. If this data is requested, the user is required to free it with "dogleg_freeContext" when
done.
"dogleg_optimize" returns norm2( x ) at the minimum, or a negative value if an error occurred.
dogleg_freeContext
Used to deallocate memory used for an optimization cycle. Defined as:
void dogleg_freeContext(dogleg_solverContext_t** ctx);
If a pointer to a context is not requested (by passing "returnContext = NULL" to "dogleg_optimize"),
libdogleg calls this routine automatically. If the user did retrieve this pointer, though, it must be
freed with "dogleg_freeContext" when the user is finished.
dogleg_testGradient
libdogleg requires the user to compute the jacobian matrix J. This is a performance optimization, since J
could be computed by differences of x. This optimization is often worth the extra effort, but it creates
a possibility that J will have a mistake and J = df/dp would not be true. To find these types of issues,
the user can call
void dogleg_testGradient(unsigned int var, const double* p0,
unsigned int Nstate, unsigned int Nmeas, unsigned int NJnnz,
dogleg_callback_t* f, void* cookie);
This function computes the jacobian with center differences and compares the result with the jacobian
computed by the callback function. It is recommended to do this for every variable while developing the
program that uses libdogleg.
• "var" is the index of the variable being tested
• "p0" is the state vector p where we're evaluating the jacobian
• "Nstate", "Nmeas", "NJnnz" are the number of state variables, measurements and non-zero jacobian
elements, as before
• "f" is the callback function, as before
• "cookie" is the user data, as before
This function returns nothing, but prints out the test results.
dogleg_callback_t
The main user callback that specifies the optimization problem has type
typedef void (dogleg_callback_t)(const double* p,
double* x,
cholmod_sparse* Jt,
void* cookie);
• p is the current state vector
• x is the resulting f(p)
• Jt is the transpose of df/dp at p. As mentioned previously, Jt is stored column-first by CHOLMOD,
which can be interpreted as storing J row-first by the user-defined callback routine
• The "cookie" is the user-defined arbitrary data passed into "dogleg_optimize".
dogleg_solverContext_t
This is the solver context that can be retrieved through the "returnContext" parameter of the
"dogleg_optimize" call. This structure contains all the internal state used by the solver. If requested,
the user is responsible for calling "dogleg_freeContext" when done. This structure is defined as:
typedef struct
{
cholmod_common common;
dogleg_callback_t* f;
void* cookie;
// between steps, beforeStep contains the operating point of the last step.
// afterStep is ONLY used while making the step. Externally, use beforeStep
// unless you really know what you're doing
dogleg_operatingPoint_t* beforeStep;
dogleg_operatingPoint_t* afterStep;
// The result of the last JtJ factorization performed. Note that JtJ is not
// necessarily factorized at every step, so this is NOT guaranteed to contain
// the factorization of the most recent JtJ
cholmod_factor* factorization;
// Have I ever seen a singular JtJ? If so, I add a small constant to the
// diagonal from that point on. This is a simple and fast way to deal with
// singularities. This is suboptimal but works for me for now.
int wasPositiveSemidefinite;
} dogleg_solverContext_t;
Some of the members are copies of the data passed into "dogleg_optimize"; some others are internal state.
Of potential interest are
• "common" is a cholmod_common structure used by all CHOLMOD calls. This can be used for any extra
CHOLMOD work the user may want to do
• "beforeStep" contains the operating point of the optimum solution. The user can analyze this data
without the need to re-call the callback routine.
dogleg_operatingPoint_t
An operating point of the solver. This is a part of "dogleg_solverContext_t". Various variables
describing the operating point such as p, J, x, norm2(x) and Jt x are available. All of the just-
mentioned variables are computed at every step and are thus always valid.
// an operating point of the solver
typedef struct
{
double* p;
double* x;
double norm2_x;
cholmod_sparse* Jt;
double* Jt_x;
// the cached update vectors. It's useful to cache these so that when a step is rejected, we can
// reuse these when we retry
double* updateCauchy;
cholmod_dense* updateGN_cholmoddense;
double updateCauchy_lensq, updateGN_lensq; // update vector lengths
// whether the current update vectors are correct or not
int updateCauchy_valid, updateGN_valid;
int didStepToEdgeOfTrustRegion;
} dogleg_operatingPoint_t;
Parameters
It is not required to call any of these, but it's highly recommended to set the initial trust-region size
and the termination thresholds to match the problem being solved. Furthermore, it's highly recommended
for the problem being solved to be scaled so that every state variable affects the objective norm2( x )
equally. This makes this method's concept of "trust region" much more well-defined and makes the
termination criteria work correctly.
dogleg_setMaxIterations
To set the maximum number of solver iterations, call
void dogleg_setMaxIterations(int n);
dogleg_setDebug
To turn on debug output, call
void dogleg_setDebug(int debug);
with a non-zero value for "debug". By default, debug output is disabled.
dogleg_setInitialTrustregion
The optimization method keeps track of a trust region size. Here, the trust region is a ball in R^Nstate.
When the method takes a step p -> p + delta_p, it makes sure that
sqrt( norm2( delta_p ) ) < trust region size.
The initial value of the trust region size can be set with
void dogleg_setInitialTrustregion(double t);
The dogleg algorithm is efficient when recomputing a rejected step for a smaller trust region, so set the
initial trust region size to a value larger to a reasonable estimate; the method will quickly shrink the
trust region to the correct size.
dogleg_setThresholds
The routine exits when the maximum number of iterations is exceeded, or a termination threshold is hit,
whichever happens first. The termination thresholds are all designed to trigger when very slow progress
is being made. If all went well, this slow progress is due to us finding the optimum. There are 3
termination thresholds:
• The function being minimized is E = norm2( x ) where x = f(p).
dE/dp = 2*Jt*x where Jt is transpose(dx/dp).
if( for every i fabs(Jt_x[i]) < JT_X_THRESHOLD )
{ we are done; }
• The method takes discrete steps: p -> p + delta_p
if( for every i fabs(delta_p[i]) < UPDATE_THRESHOLD)
{ we are done; }
• The method dynamically controls the trust region.
if(trustregion < TRUSTREGION_THRESHOLD)
{ we are done; }
To set these threholds, call
void dogleg_setThresholds(double Jt_x, double update, double trustregion);
To leave a particular threshold alone, specify a negative value.
dogleg_setTrustregionUpdateParameters
This function sets the parameters that control when and how the trust region is updated. The default
values should work well in most cases, and shouldn't need to be tweaked.
Declaration looks like
void dogleg_setTrustregionUpdateParameters(double downFactor, double downThreshold,
double upFactor, double upThreshold);
To see what the parameters do, look at "evaluateStep_adjustTrustRegion" in the source. Again, these
should just work as is.
BUGS
The current implementation doesn't handle a singular JtJ gracefully (JtJ = Jt * J). Analytically, JtJ is
at worst positive semi-definite (has 0 eigenvalues). If a singular JtJ is ever encountered, from that
point on, JtJ + lambda*I is inverted instead for some positive constant lambda. This makes the matrix
strictly positive definite, but is sloppy. At least I should vary lambda. In my current applications, a
singular JtJ only occurs if at a particular operating point the vector x has no dependence at all on some
elements of p. In the general case other causes could exist, though.
There's an inefficiency in that the callback always returns x and J. When I evaluate and reject a step, I
do not end up using J at all. Dependng on the callback function, it may be better to ask for x and then,
if the step is accepted, to ask for J.
AUTHOR
Dima Kogan, "<dima@secretsauce.net>"
LICENSE AND COPYRIGHT
Copyright 2011 Oblong Industries
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser
General Public License as published by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General
Public License for more details.
The full text of the license is available at <http://www.gnu.org/licenses>