simpeg.objective_function.ComboObjectiveFunction#
- class simpeg.objective_function.ComboObjectiveFunction(objfcts=None, multipliers=None, unpack_on_add=True)[source]#
Bases:
BaseObjectiveFunction
Composite for multiple objective functions.
This class allows the creation of an objective function \(\phi\) which is the sum of a list of other objective functions \(\phi_i\). Each objective function has associated with it a multiplier \(c_i\) such that
\[\phi = \sum_{i = 1}^N c_i \phi_i\]- Parameters:
- objfcts
None
orlist
of
simpeg.objective_function.BaseObjectiveFunction
,optional
List containing the objective functions that will live inside the composite class. If
None
, an empty list will be created.- multipliers
None
orlist
of
int
,optional
List containing the multipliers for each objective function in
objfcts
. IfNone
, a list full of ones with the same length asobjfcts
will be created.- unpack_on_addbool
Whether to unpack the multiple objective functions when adding them to another objective function, or to add them as a whole.
- objfcts
Attributes
Full weighting matrix for the combo objective function.
Mapping from the model to the quantity evaluated in the object function.
Multipliers for the objective functions.
Number of model parameters.
Methods
__call__
(m[, f])Evaluate the objective functions for a given model.
deriv
(m[, f])Gradient of the objective function evaluated for the model provided.
deriv2
(m[, v, f])Hessian of the objective function evaluated for the model provided.
get_functions_of_type
(fun_class)Return objective functions of a given type(s).
map_class
alias of
IdentityMap
test
([x, num, random_seed])Run a convergence test on both the first and second derivatives.
Examples
Build a simple combo objective function:
>>> objective_fun_a = L2ObjectiveFunction(nP=3) >>> objective_fun_b = L2ObjectiveFunction(nP=3) >>> combo = ComboObjectiveFunction([objective_fun_a, objective_fun_b], [1, 0.5]) >>> print(len(combo)) 2 >>> print(combo.multipliers) [1, 0.5]
Combo objective functions are also created after adding two objective functions:
>>> combo = 2 * objective_fun_a + 3.5 * objective_fun_b >>> print(len(combo)) 2 >>> print(combo.multipliers) [2, 3.5]
We could add two combo objective functions as well:
>>> objective_fun_c = L2ObjectiveFunction(nP=3) >>> objective_fun_d = L2ObjectiveFunction(nP=3) >>> combo_1 = 4.3 * objective_fun_a + 3 * objective_fun_b >>> combo_2 = 1.5 * objective_fun_c + 0.5 * objective_fun_d >>> combo = combo_1 + combo_2 >>> print(len(combo)) 4 >>> print(combo.multipliers) [4.3, 3, 1.5, 0.5]
We can choose to not unpack the objective functions when creating the combo. For example:
>>> objective_fun_a = L2ObjectiveFunction(nP=3) >>> objective_fun_b = L2ObjectiveFunction(nP=3) >>> objective_fun_c = L2ObjectiveFunction(nP=3) >>> >>> # Create a ComboObjectiveFunction that won't unpack >>> combo_1 = ComboObjectiveFunction( ... objfcts=[objective_fun_a, objective_fun_b], ... multipliers=[0.1, 1.2], ... unpack_on_add=False, ... ) >>> combo_2 = combo_1 + objective_fun_c >>> print(len(combo_2)) 2
Galleries and Tutorials using simpeg.objective_function.ComboObjectiveFunction
#
PF: Gravity: Tiled Inversion Linear
Magnetic inversion on a TreeMesh with remanence
Magnetic inversion on a TreeMesh
Magnetic Amplitude inversion on a TreeMesh
3D DC inversion of Dipole Dipole array
Parametric DC inversion with Dipole Dipole array
2D inversion of Loop-Loop EM Data
EM: TDEM: 1D: Inversion with VTEM waveform
Petrophysically guided inversion (PGI): Linear example
Petrophysically guided inversion: Joint linear example with nonlinear relationships
Heagy et al., 2017 1D RESOLVE and SkyTEM Bookpurnong Inversions
Heagy et al., 2017 1D RESOLVE Bookpurnong Inversion
Heagy et al., 2017 1D FDEM and TDEM inversions
PF: Gravity: Laguna del Maule Bouguer Gravity
Straight Ray with Volume Data Misfit Term
1D Inversion of Time-Domain Data for a Single Sounding
2.5D DC Resistivity and IP Least-Squares Inversion
3D Least-Squares Inversion of DC and IP Data
1D Inversion of for a Single Sounding
Least-Squares Inversion of Gravity Anomaly Data
Sparse Norm Inversion of Gravity Anomaly Data
Compare weighting strategy with Inversion of surface Gravity Anomaly Data
Least-Squares 1D Inversion of Sounding Data
Sparse 1D Inversion of Sounding Data
Parametric 1D Inversion of Sounding Data
2.5D DC Resistivity Least-Squares Inversion
2.5D DC Resistivity Inversion with Sparse Norms
3D Least-Squares Inversion of DC Resistivity Data
Sparse Norm Inversion for Total Magnetic Intensity Data on a Tensor Mesh
Linear Least-Squares Inversion
Sparse Inversion with Iteratively Re-Weighted Least-Squares
Sparse Norm Inversion of 2D Seismic Tomography Data
Joint PGI of Gravity + Magnetic on an Octree mesh using full petrophysical information
Joint PGI of Gravity + Magnetic on an Octree mesh without petrophysical information
Cross-gradient Joint Inversion of Gravity and Magnetic Anomaly Data