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Update documentation
Update the linear solver documentation thoroughly as it had bit rotted and was flat out wrong in some places and incomplete in others. https://github.com/ceres-solver/ceres-solver/issues/865 https://github.com/ceres-solver/ceres-solver/issues/862 Change-Id: Ic395efabd0589a401e2b971c45869bd881b68a34
This commit is contained in:
Sameer Agarwal
+49
-61
@@ -64,8 +64,6 @@ class CERES_EXPORT Solver {
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// with a message describing the problem.
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bool IsValid(std::string* error) const;
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// Minimizer options ----------------------------------------
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// Ceres supports the two major families of optimization strategies -
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// Trust Region and Line Search.
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//
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@@ -571,13 +569,6 @@ class CERES_EXPORT Solver {
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// This settings only affects the SPARSE_NORMAL_CHOLESKY solver.
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bool dynamic_sparsity = false;
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// TODO(sameeragarwal): Further expand the documentation for the
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// following two options.
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// TODO(joydeepbiswas): Update the documentation for the mixed precision
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// option with CUDA.
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// NOTE1: EXPERIMENTAL FEATURE, UNDER DEVELOPMENT, USE AT YOUR OWN RISK.
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//
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// If use_mixed_precision_solves is true, the Gauss-Newton matrix
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// is computed in double precision, but its factorization is
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// computed in single precision. This can result in significant
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@@ -588,16 +579,57 @@ class CERES_EXPORT Solver {
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// If use_mixed_precision_solves is true, we recommend setting
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// max_num_refinement_iterations to 2-3.
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//
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// NOTE2: The following two options are currently only applicable
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// if sparse_linear_algebra_library_type is EIGEN_SPARSE or
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// ACCELERATE_SPARSE, and linear_solver_type is SPARSE_NORMAL_CHOLESKY
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// or SPARSE_SCHUR.
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// This options is available when linear solver uses sparse or dense
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// cholesky factorization, except when sparse_linear_algebra_library_type =
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// SUITE_SPARSE.
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bool use_mixed_precision_solves = false;
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// Number steps of the iterative refinement process to run when
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// computing the Gauss-Newton step.
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int max_num_refinement_iterations = 0;
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// Minimum number of iterations for which the linear solver should
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// run, even if the convergence criterion is satisfied.
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int min_linear_solver_iterations = 0;
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// Maximum number of iterations for which the linear solver should
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// run. If the solver does not converge in less than
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// max_linear_solver_iterations, then it returns MAX_ITERATIONS,
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// as its termination type.
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int max_linear_solver_iterations = 500;
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// Maximum number of iterations performed by SCHUR_POWER_SERIES_EXPANSION.
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// Each iteration corresponds to one more term in the power series expansion
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// od the inverse of the Schur complement. This value controls the maximum
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// number of iterations whether it is used as a preconditioner or just to
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// initialize the solution for ITERATIVE_SCHUR.
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int max_num_spse_iterations = 5;
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// Use SCHUR_POWER_SERIES_EXPANSION to initialize the solution for
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// ITERATIVE_SCHUR. This option can be set true regardless of what
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// preconditioner is being used.
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bool use_spse_initialization = false;
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// When use_spse_initialization is true, this parameter along with
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// max_num_spse_iterations controls the number of
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// SCHUR_POWER_SERIES_EXPANSION iterations performed for initialization. It
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// is not used to control the preconditioner.
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double spse_tolerance = 0.1;
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// Forcing sequence parameter. The truncated Newton solver uses
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// this number to control the relative accuracy with which the
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// Newton step is computed.
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//
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// This constant is passed to ConjugateGradientsSolver which uses
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// it to terminate the iterations when
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//
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// (Q_i - Q_{i-1})/Q_i < eta/i
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double eta = 1e-1;
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// Normalize the jacobian using Jacobi scaling before calling
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// the linear least squares solver.
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bool jacobi_scaling = true;
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// Some non-linear least squares problems have additional
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// structure in the way the parameter blocks interact that it is
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// beneficial to modify the way the trust region step is computed.
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@@ -681,49 +713,6 @@ class CERES_EXPORT Solver {
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// iterations is disabled.
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double inner_iteration_tolerance = 1e-3;
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// Minimum number of iterations for which the linear solver should
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// run, even if the convergence criterion is satisfied.
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int min_linear_solver_iterations = 0;
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// Maximum number of iterations for which the linear solver should
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// run. If the solver does not converge in less than
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// max_linear_solver_iterations, then it returns MAX_ITERATIONS,
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// as its termination type.
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int max_linear_solver_iterations = 500;
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// Maximum number of iterations performed by SCHUR_POWER_SERIES_EXPANSION.
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// This value controls the maximum number of iterations whether it is used
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// as a preconditioner or just to initialize the solution for
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// ITERATIVE_SCHUR.
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int max_num_spse_iterations = 5;
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// Use SCHUR_POWER_SERIES_EXPANSION to initialize the solution for
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// ITERATIVE_SCHUR. This option can be set true regardless of what
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// preconditioner is being used.
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bool use_spse_initialization = false;
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// When use_spse_initialization is true, this parameter along with
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// max_num_spse_iterations controls the number of
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// SCHUR_POWER_SERIES_EXPANSION iterations performed for initialization. It
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// is not used to control the preconditioner.
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double spse_tolerance = 0.1;
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// Forcing sequence parameter. The truncated Newton solver uses
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// this number to control the relative accuracy with which the
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// Newton step is computed.
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//
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// This constant is passed to ConjugateGradientsSolver which uses
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// it to terminate the iterations when
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//
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// (Q_i - Q_{i-1})/Q_i < eta/i
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double eta = 1e-1;
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// Normalize the jacobian using Jacobi scaling before calling
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// the linear least squares solver.
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bool jacobi_scaling = true;
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// Logging options ---------------------------------------------------------
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LoggingType logging_type = PER_MINIMIZER_ITERATION;
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// By default the Minimizer progress is logged to VLOG(1), which
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@@ -860,10 +849,9 @@ class CERES_EXPORT Solver {
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// IterationSummary for each minimizer iteration in order.
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std::vector<IterationSummary> iterations;
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// Number of minimizer iterations in which the step was
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// accepted. Unless use_non_monotonic_steps is true this is also
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// the number of steps in which the objective function value/cost
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// went down.
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// Number of minimizer iterations in which the step was accepted. Unless
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// use_nonmonotonic_steps is true this is also the number of steps in which
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// the objective function value/cost went down.
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int num_successful_steps = -1;
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// Number of minimizer iterations in which the step was rejected
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@@ -1077,7 +1065,7 @@ class CERES_EXPORT Solver {
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PreconditionerType preconditioner_type_used = IDENTITY;
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// Type of clustering algorithm used for visibility based
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// preconditioning. Only meaningful when the preconditioner_type
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// preconditioning. Only meaningful when the preconditioner_type_used
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// is CLUSTER_JACOBI or CLUSTER_TRIDIAGONAL.
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VisibilityClusteringType visibility_clustering_type = CANONICAL_VIEWS;
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