# Problem class# class pygmo.problem(udp=null_problem())#

Problem class.

This class represents a generic mathematical programming or evolutionary optimization problem in the form:

$\begin{split}\begin{array}{rl} \mbox{find:} & \mathbf {lb} \le \mathbf x \le \mathbf{ub}\\ \mbox{to minimize: } & \mathbf f(\mathbf x, s) \in \mathbb R^{n_{obj}}\\ \mbox{subject to:} & \mathbf {c}_e(\mathbf x, s) = 0 \\ & \mathbf {c}_i(\mathbf x, s) \le 0 \end{array}\end{split}$

where $$\mathbf x \in \mathbb R^{n_{cx}} \times \mathbb Z^{n_{ix}}$$ is called decision vector or chromosome, and is made of $$n_{cx}$$ real numbers and $$n_{ix}$$ integers (all represented as doubles). The total problem dimension is then indicated with $$n_x = n_{cx} + n_{ix}$$. $$\mathbf{lb}, \mathbf{ub} \in \mathbb R^{n_{cx}} \times \mathbb Z^{n_{ix}}$$ are the box-bounds, $$\mathbf f: \mathbb R^{n_{cx}} \times \mathbb Z^{n_{ix}} \rightarrow \mathbb R^{n_{obj}}$$ define the objectives, $$\mathbf c_e: \mathbb R^{n_{cx}} \times \mathbb Z^{n_{ix}} \rightarrow \mathbb R^{n_{ec}}$$ are non linear equality constraints, and $$\mathbf c_i: \mathbb R^{n_{cx}} \times \mathbb Z^{n_{ix}} \rightarrow \mathbb R^{n_{ic}}$$ are non linear inequality constraints. Note that the objectives and constraints may also depend from an added value $$s$$ seeding the values of any number of stochastic variables. This allows also for stochastic programming tasks to be represented by this class. A tolerance is also considered for all constraints and set, by default, to zero. It can be modified via the c_tol attribute.

In order to define an optimizaztion problem in pygmo, the user must first define a class whose methods describe the properties of the problem and allow to compute the objective function, the gradient, the constraints, etc. In pygmo, we refer to such a class as a user-defined problem, or UDP for short. Once defined and instantiated, a UDP can then be used to construct an instance of this class, problem, which provides a generic interface to optimization problems.

Every UDP must implement at least the following two methods:

def fitness(self, dv):
...
def get_bounds(self):
...


The fitness() method is expected to return the fitness of the input decision vector (concatenating the objectives, the equality and the inequality constraints), while get_bounds() is expected to return the box bounds of the problem, $$(\mathbf{lb}, \mathbf{ub})$$, which also implicitly define the dimension of the problem. The fitness() and get_bounds() methods of the UDP are accessible from the corresponding pygmo.problem.fitness() and pygmo.problem.get_bounds() methods (see their documentation for information on how the two methods should be implemented in the UDP and other details).

The two mandatory methods above allow to define a single objective, deterministic, derivative-free, unconstrained optimization problem. In order to consider more complex cases, the UDP may implement one or more of the following methods:

def get_nobj(self):
...
def get_nec(self):
...
def get_nic(self):
...
def get_nix(self):
...
def batch_fitness(self, dvs):
...
def has_batch_fitness(self):
...
...
...
...
...
def has_hessians(self):
...
def hessians(self, dv):
...
def has_hessians_sparsity(self):
...
def hessians_sparsity(self):
...
def has_set_seed(self):
...
def set_seed(self, s):
...
def get_name(self):
...
def get_extra_info(self):
...


See the documentation of the corresponding methods in this class for details on how the optional methods in the UDP should be implemented and on how they are used by problem. Note that the exposed C++ problems can also be used as UDPs, even if they do not expose any of the mandatory or optional methods listed above (see here for the full list of UDPs already coded in pygmo).

This class is the Python counterpart of the C++ class pagmo::problem.

Parameters

udp – a user-defined problem, either C++ or Python

Raises
• NotImplementedError – if udp does not implement the mandatory methods detailed above

• ValueError – if the number of objectives of the UDP is zero, the number of objectives, equality or inequality constraints is larger than an implementation-defined value, the problem bounds are invalid (e.g., they contain NaNs, the dimensionality of the lower bounds is different from the dimensionality of the upper bounds, etc. - note that infinite bounds are allowed), or if the gradient_sparsity() and hessians_sparsity() methods of the UDP fail basic sanity checks (e.g., they return vectors with repeated indices, they contain indices exceeding the problem’s dimensions, etc.)

• unspecified – any exception thrown by methods of the UDP invoked during construction, the deep copy of the UDP, the constructor of the underlying C++ class, failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

batch_fitness(dvs)#

This method implements the evaluation of multiple decision vectors in batch mode by invoking the batch_fitness() method of the UDP. The batch_fitness() method of the UDP accepts in input a batch of decision vectors, dvs, stored contiguously: for a problem with dimension $$n$$, the first decision vector in dvs occupies the index range $$\left[0, n\right)$$, the second decision vector occupies the range $$\left[n, 2n\right)$$, and so on. The return value is the batch of fitness vectors fvs resulting from computing the fitness of the input decision vectors. fvs is also stored contiguously: for a problem with fitness dimension $$f$$, the first fitness vector will occupy the index range $$\left[0, f\right)$$, the second fitness vector will occupy the range $$\left[f, 2f\right)$$, and so on.

If the UDP provides a batch_fitness() method, this method will forward dvs to the batch_fitness() method of the UDP after sanity checks. The output of the batch_fitness() method of the UDP will also be checked before being returned. If the UDP does not provide a batch_fitness() method, an error will be raised.

A successful call of this method will increase the internal fitness evaluation counter (see get_fevals()).

The batch_fitness() method of the UDP must be able to take as input the decision vectors as a 1D NumPy array, and it must return the fitness vectors as an iterable Python object (e.g., 1D NumPy array, list, tuple, etc.).

Parameters

dvs (array-like object) – the decision vectors (chromosomes) to be evaluated in batch mode

Returns

the fitness vectors of dvs

Return type

1D NumPy float array

Raises
• ValueError – if dvs and/or the return value are not compatible with the problem’s properties

• unspecified – any exception thrown by the batch_fitness() method of the UDP, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

property c_tol#

Constraints tolerance.

This property contains an array of float that are used when checking for constraint feasibility. The dimension of the array is $$n_{ec} + n_{ic}$$ (i.e., the total number of constraints), and the array is zero-filled on problem construction.

This property can also be set via a scalar, instead of an array. In such case, all the tolerances will be set to the provided scalar value.

Returns

the constraints’ tolerances

Return type

1D NumPy float array

Raises
• ValueError – if, when setting this property, the size of the input array differs from the number of constraints of the problem or if any element of the array is negative or NaN

• unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

Examples

>>> from pygmo import problem, hock_schittkowski_71 as hs71
>>> prob = problem(hs71())
>>> prob.c_tol
array([0., 0.])
>>> prob.c_tol = [1, 2]
>>> prob.c_tol
array([1., 2.])
>>> prob.c_tol = .5
>>> prob.c_tol
array([0.5, 0.5])

extract(t)#

Extract the user-defined problem.

This method allows to extract a reference to the user-defined problem (UDP) stored within this problem instance. The behaviour of this function depends on the value of t (which must be a type) and on the type of the internal UDP:

Parameters

t (type) – the type of the user-defined problem to extract

Returns

a reference to the internal user-defined problem, or None if the extraction fails

Raises

TypeError – if t is not a type

Examples

>>> import pygmo as pg
>>> p1 = pg.problem(pg.rosenbrock())
>>> p1.extract(pg.rosenbrock)
<pygmo.core.rosenbrock at 0x7f56b870fd50>
>>> p1.extract(pg.ackley) is None
True
>>> class prob:
...     def fitness(self, x):
...         return [x]
...     def get_bounds(self):
...         return (,)
>>> p2 = pg.problem(prob())
>>> p2.extract(object)
<__main__.prob at 0x7f56a66b6588>
>>> p2.extract(prob)
<__main__.prob at 0x7f56a66b6588>
>>> p2.extract(pg.rosenbrock) is None
True

feasibility_f(f)#

This method will check the feasibility of a fitness vector f against the tolerances returned by c_tol.

Parameters

f (array-like object) – a fitness vector

Returns

True if the fitness vector is feasible, False otherwise

Return type

bool

Raises

ValueError – if the size of f is not the same as the output of get_nf()

feasibility_x(x)#

This method will check the feasibility of the fitness corresponding to a decision vector x against the tolerances returned by c_tol.

This will cause one fitness evaluation.

Parameters

dv (array-like object) – a decision vector

Returns

True if x results in a feasible fitness, False otherwise

Return type

bool

Raises

unspecified – any exception thrown by feasibility_f() or fitness()

fitness(dv)#

Fitness.

This method will invoke the fitness() method of the UDP to compute the fitness of the input decision vector dv. The return value of the fitness() method of the UDP is expected to have a dimension of $$n_{f} = n_{obj} + n_{ec} + n_{ic}$$ and to contain the concatenated values of $$\mathbf f, \mathbf c_e$$ and $$\mathbf c_i$$ (in this order). Equality constraints are all assumed in the form $$c_{e_i}(\mathbf x) = 0$$ while inequalities are assumed in the form $$c_{i_i}(\mathbf x) <= 0$$ so that negative values are associated to satisfied inequalities.

In addition to invoking the fitness() method of the UDP, this method will perform sanity checks on dv and on the returned fitness vector. A successful call of this method will increase the internal fitness evaluation counter (see get_fevals()).

The fitness() method of the UDP must be able to take as input the decision vector as a 1D NumPy array, and it must return the fitness vector as an iterable Python object (e.g., 1D NumPy array, list, tuple, etc.).

Parameters

dv (array-like object) – the decision vector (chromosome) to be evaluated

Returns

the fitness of dv

Return type

1D NumPy float array

Raises
• ValueError – if either the length of dv differs from the value returned by get_nx(), or the length of the returned fitness vector differs from the value returned by get_nf()

• unspecified – any exception thrown by the fitness() method of the UDP, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

get_bounds()#

Box-bounds.

This method will return the box-bounds $$(\mathbf{lb}, \mathbf{ub})$$ of the problem, as returned by the get_bounds() method of the UDP. Infinities in the bounds are allowed.

The get_bounds() method of the UDP must return the box-bounds as a tuple of 2 elements, the lower bounds vector and the upper bounds vector, which must be represented as iterable Python objects (e.g., 1D NumPy arrays, lists, tuples, etc.). The box-bounds returned by the UDP are checked upon the construction of a problem.

Returns

a tuple of two 1D NumPy float arrays representing the lower and upper box-bounds of the problem

Return type

tuple

Raises

unspecified – any exception thrown by the invoked method of the underlying C++ class, or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

get_extra_info()#

Problem’s extra info.

If the UDP provides a get_extra_info() method, then this method will return the output of its get_extra_info() method. Otherwise, an empty string will be returned.

Returns

Return type

str

Raises

unspecified – any exception thrown by the get_extra_info() method of the UDP

get_fevals()#

Number of fitness evaluations.

Each time a call to fitness() successfully completes, an internal counter is increased by one. The counter is initialised to zero upon problem construction and it is never reset. Copy operations copy the counter as well.

Returns

the number of times fitness() was successfully called

Return type

int

get_gevals()#

Each time a call to gradient() successfully completes, an internal counter is increased by one. The counter is initialised to zero upon problem construction and it is never reset. Copy operations copy the counter as well.

Returns

the number of times gradient() was successfully called

Return type

int

get_hevals()#

Number of hessians evaluations.

Each time a call to hessians() successfully completes, an internal counter is increased by one. The counter is initialised to zero upon problem construction and it is never reset. Copy operations copy the counter as well.

Returns

the number of times hessians() was successfully called

Return type

int

get_lb()#

Lower box-bounds.

This method will return the lower box-bounds for this problem. See get_bounds() for a detailed explanation of how the bounds are determined.

Returns

an array representing the lower box-bounds of this problem

Return type

1D NumPy float array

Raises

unspecified – any exception thrown by the invoked method of the underlying C++ class, or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

get_name()#

Problem’s name.

If the UDP provides a get_name() method, then this method will return the output of its get_name() method. Otherwise, an implementation-defined name based on the type of the UDP will be returned.

Returns

the problem’s name

Return type

str

get_nc()#

Total number of constraints.

This method will return the sum of the output of get_nic() and get_nec() (i.e., the total number of constraints).

Returns

the total number of constraints of the problem

Return type

int

get_ncx()#

Continuous dimension of the problem.

This method will return $$n_{cx}$$, the continuous dimension of the problem.

Returns

the continuous dimension of the problem

Return type

int

get_nec()#

Number of equality constraints.

This method will return $$n_{ec}$$, the number of equality constraints of the problem.

The optional get_nec() method of the UDP must return the number of equality constraints as an int. If the UDP does not implement the get_nec() method, zero equality constraints will be assumed. The number of equality constraints returned by the UDP is checked upon the construction of a problem.

Returns

the number of equality constraints of the problem

Return type

int

get_nf()#

Dimension of the fitness.

This method will return $$n_{f}$$, the dimension of the fitness, which is the sum of $$n_{obj}$$, $$n_{ec}$$ and $$n_{ic}$$.

Returns

the dimension of the fitness

Return type

int

get_nic()#

Number of inequality constraints.

This method will return $$n_{ic}$$, the number of inequality constraints of the problem.

The optional get_nic() method of the UDP must return the number of inequality constraints as an int. If the UDP does not implement the get_nic() method, zero inequality constraints will be assumed. The number of inequality constraints returned by the UDP is checked upon the construction of a problem.

Returns

the number of inequality constraints of the problem

Return type

int

get_nix()#

Integer dimension of the problem.

This method will return $$n_{ix}$$, the integer dimension of the problem.

The optional get_nix() method of the UDP must return the problem’s integer dimension as an int. If the UDP does not implement the get_nix() method, a zero integer dimension will be assumed. The integer dimension returned by the UDP is checked upon the construction of a problem.

Returns

the integer dimension of the problem

Return type

int

get_nobj()#

Number of objectives.

This method will return $$n_{obj}$$, the number of objectives of the problem.

The optional get_nobj() method of the UDP must return the number of objectives as an int. If the UDP does not implement the get_nobj() method, a single-objective optimizaztion problem will be assumed. The number of objectives returned by the UDP is checked upon the construction of a problem.

Returns

the number of objectives of the problem

Return type

int

get_nx()#

Dimension of the problem.

This method will return $$n_{x}$$, the dimension of the problem as established by the length of the bounds returned by get_bounds().

Returns

the dimension of the problem

Return type

int

This method will return a value of the enum pygmo.thread_safety which indicates the thread safety level of the UDP. Unlike in C++, in Python it is not possible to re-implement this method in the UDP. That is, for C++ UDPs, the returned value will be the value returned by the get_thread_safety() method of the UDP. For Python UDPs, the returned value will be unconditionally none.

Returns

the thread safety level of the UDP

Return type

a value of pygmo.thread_safety

get_ub()#

Upper box-bounds.

This method will return the upper box-bounds for this problem. See get_bounds() for a detailed explanation of how the bounds are determined.

Returns

an array representing the upper box-bounds of this problem

Return type

1D NumPy float array

Raises

unspecified – any exception thrown by the invoked method of the underlying C++ class, or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

This method will compute the gradient of the input decision vector dv by invoking the gradient() method of the UDP. The gradient() method of the UDP must return a sparse representation of the gradient: the $$k$$-th term of the gradient vector is expected to contain $$\frac{\partial f_i}{\partial x_j}$$, where the pair $$(i,j)$$ is the $$k$$-th element of the sparsity pattern (collection of index pairs), as returned by gradient_sparsity().

If the UDP provides a gradient() method, this method will forward dv to the gradient() method of the UDP after sanity checks. The output of the gradient() method of the UDP will also be checked before being returned. If the UDP does not provide a gradient() method, an error will be raised. A successful call of this method will increase the internal gradient evaluation counter (see get_gevals()).

The gradient() method of the UDP must be able to take as input the decision vector as a 1D NumPy array, and it must return the gradient vector as an iterable Python object (e.g., 1D NumPy array, list, tuple, etc.).

Parameters

dv (array-like object) – the decision vector whose gradient will be computed

Returns

Return type

1D NumPy float array

Raises
• ValueError – if either the length of dv differs from the value returned by get_nx(), or the returned gradient vector does not have the same size as the vector returned by gradient_sparsity()

• NotImplementedError – if the UDP does not provide a gradient() method

• unspecified – any exception thrown by the gradient() method of the UDP, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

This method will return the gradient sparsity pattern of the problem. The gradient sparsity pattern is a lexicographically sorted collection of the indices $$(i,j)$$ of the non-zero elements of $$g_{ij} = \frac{\partial f_i}{\partial x_j}$$.

If has_gradient_sparsity() returns True, then the gradient_sparsity() method of the UDP will be invoked, and its result returned (after sanity checks). Otherwise, a a dense pattern is assumed and the returned vector will be $$((0,0),(0,1), ... (0,n_x-1), ...(n_f-1,n_x-1))$$.

The gradient_sparsity() method of the UDP must return either a 2D NumPy array of integers, or an iterable Python object of any kind. Specifically:

• if the returned value is a NumPy array, its shape must be $$(n,2)$$ (with $$n \geq 0$$),

• if the returned value is an iterable Python object, then its elements must in turn be iterable Python objects containing each exactly 2 elements representing the indices $$(i,j)$$.

Returns

Return type

2D Numpy int array

Raises
• ValueError – if the NumPy array returned by the UDP does not satisfy the requirements described above (e.g., invalid shape, dimensions, etc.), at least one element of the returned iterable Python object does not consist of a collection of exactly 2 elements, or the sparsity pattern returned by the UDP is invalid (specifically, if it is not strictly sorted lexicographically, or if the indices in the pattern are incompatible with the properties of the problem, or if the size of the returned pattern is different from the size recorded upon construction)

• OverflowError – if the NumPy array returned by the UDP contains integer values which are negative or outside an implementation-defined range

• unspecified – any exception thrown by the underlying C++ function, the PyArray_FROM_OTF() function from the NumPy C API, or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

has_batch_fitness()#

Check if the batch_fitness() method is available in the UDP.

This method will return True if the batch_fitness() method is available in the UDP, False otherwise.

The availability of the batch_fitness() method is determined as follows:

• if the UDP does not provide a batch_fitness() method, then this method will always return False;

• if the UDP provides a batch_fitness() method but it does not provide a has_batch_fitness() method, then this method will always return True;

• if the UDP provides both a batch_fitness() and a has_batch_fitness() method, then this method will return the output of the has_batch_fitness() method of the UDP.

The optional has_batch_fitness() method of the UDP must return a bool. For information on how to implement the batch_fitness() method of the UDP, see batch_fitness().

Returns

a flag signalling the availability of the batch_fitness() method in the UDP

Return type

bool

Check if the gradient is available in the UDP.

This method will return True if the gradient is available in the UDP, False otherwise.

The availability of the gradient is determined as follows:

• if the UDP does not provide a gradient() method, then this method will always return False;

• if the UDP provides a gradient() method but it does not provide a has_gradient() method, then this method will always return True;

• if the UDP provides both a gradient() and a has_gradient() method, then this method will return the output of the has_gradient() method of the UDP.

The optional has_gradient() method of the UDP must return a bool. For information on how to implement the gradient() method of the UDP, see gradient().

Returns

a flag signalling the availability of the gradient in the UDP

Return type

bool

Check if the gradient sparsity is available in the UDP.

This method will return True if the gradient sparsity is available in the UDP, False otherwise.

The availability of the gradient sparsity is determined as follows:

• if the UDP does not provide a gradient_sparsity() method, then this method will always return False;

• if the UDP provides a gradient_sparsity() method but it does not provide a has_gradient_sparsity() method, then this method will always return True;

• if the UDP provides both a gradient_sparsity() method and a has_gradient_sparsity() method, then this method will return the output of the has_gradient_sparsity() method of the UDP.

The optional has_gradient_sparsity() method of the UDP must return a bool. For information on how to implement the gradient_sparsity() method of the UDP, see gradient_sparsity().

Note

Regardless of what this method returns, the gradient_sparsity() method will always return a sparsity pattern: if the UDP does not provide the gradient sparsity, pygmo will assume that the sparsity pattern of the gradient is dense. See gradient_sparsity() for more details.

Returns

a flag signalling the availability of the gradient sparsity in the UDP

Return type

bool

has_hessians()#

Check if the hessians are available in the UDP.

This method will return True if the hessians are available in the UDP, False otherwise.

The availability of the hessians is determined as follows:

• if the UDP does not provide a hessians() method, then this method will always return False;

• if the UDP provides a hessians() method but it does not provide a has_hessians() method, then this method will always return True;

• if the UDP provides both a hessians() and a has_hessians() method, then this method will return the output of the has_hessians() method of the UDP.

The optional has_hessians() method of the UDP must return a bool. For information on how to implement the hessians() method of the UDP, see hessians().

Returns

a flag signalling the availability of the hessians in the UDP

Return type

bool

has_hessians_sparsity()#

Check if the hessians sparsity is available in the UDP.

This method will return True if the hessians sparsity is available in the UDP, False otherwise.

The availability of the hessians sparsity is determined as follows:

• if the UDP does not provide a hessians_sparsity() method, then this method will always return False;

• if the UDP provides a hessians_sparsity() method but it does not provide a has_hessians_sparsity() method, then this method will always return True;

• if the UDP provides both a hessians_sparsity() method and a has_hessians_sparsity() method, then this method will return the output of the has_hessians_sparsity() method of the UDP.

The optional has_hessians_sparsity() method of the UDP must return a bool. For information on how to implement the hessians_sparsity() method of the UDP, see hessians_sparsity().

Note

Regardless of what this method returns, the hessians_sparsity() method will always return a sparsity pattern: if the UDP does not provide the hessians sparsity, pygmo will assume that the sparsity pattern of the hessians is dense. See hessians_sparsity() for more details.

Returns

a flag signalling the availability of the hessians sparsity in the UDP

Return type

bool

has_set_seed()#

Check if the set_seed() method is available in the UDP.

This method will return True if the set_seed() method is available in the UDP, False otherwise.

The availability of the set_seed() method is determined as follows:

• if the UDP does not provide a set_seed() method, then this method will always return False;

• if the UDP provides a set_seed() method but it does not provide a has_set_seed() method, then this method will always return True;

• if the UDP provides both a set_seed() and a has_set_seed() method, then this method will return the output of the has_set_seed() method of the UDP.

The optional has_set_seed() method of the UDP must return a bool. For information on how to implement the set_seed() method of the UDP, see set_seed().

Returns

a flag signalling the availability of the set_seed() method in the UDP

Return type

bool

hessians(dv)#

Hessians.

This method will compute the hessians of the input decision vector dv by invoking the hessians() method of the UDP. The hessians() method of the UDP must return a sparse representation of the hessians: the element $$l$$ of the returned vector contains $$h^l_{ij} = \frac{\partial f^2_l}{\partial x_i\partial x_j}$$ in the order specified by the $$l$$-th element of the hessians sparsity pattern (a vector of index pairs $$(i,j)$$) as returned by hessians_sparsity(). Since the hessians are symmetric, their sparse representation contains only lower triangular elements.

If the UDP provides a hessians() method, this method will forward dv to the hessians() method of the UDP after sanity checks. The output of the hessians() method of the UDP will also be checked before being returned. If the UDP does not provide a hessians() method, an error will be raised. A successful call of this method will increase the internal hessians evaluation counter (see get_hevals()).

The hessians() method of the UDP must be able to take as input the decision vector as a 1D NumPy array, and it must return the hessians vector as an iterable Python object (e.g., list, tuple, etc.).

Parameters

dv (array-like object) – the decision vector whose hessians will be computed

Returns

the hessians of dv

Return type

list of 1D NumPy float array

Raises
• ValueError – if the length of dv differs from the value returned by get_nx(), or the length of returned hessians does not match the corresponding hessians sparsity pattern dimensions, or the size of the return value is not equal to the fitness dimension

• NotImplementedError – if the UDP does not provide a hessians() method

• unspecified – any exception thrown by the hessians() method of the UDP, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

hessians_sparsity()#

Hessians sparsity pattern.

This method will return the hessians sparsity pattern of the problem. Each component $$l$$ of the hessians sparsity pattern is a lexicographically sorted collection of the indices $$(i,j)$$ of the non-zero elements of $$h^l_{ij} = \frac{\partial f^l}{\partial x_i\partial x_j}$$. Since the Hessian matrix is symmetric, only lower triangular elements are allowed.

If has_hessians_sparsity() returns True, then the hessians_sparsity() method of the UDP will be invoked, and its result returned (after sanity checks). Otherwise, a dense pattern is assumed and $$n_f$$ sparsity patterns containing $$((0,0),(1,0), (1,1), (2,0) ... (n_x-1,n_x-1))$$ will be returned.

The hessians_sparsity() method of the UDP must return an iterable Python object of any kind. Each element of the returned object will then be interpreted as a sparsity pattern in the same way as described in gradient_sparsity(). Specifically:

• if the element is a NumPy array, its shape must be $$(n,2)$$ (with $$n \geq 0$$),

• if the element is itself an iterable Python object, then its elements must in turn be iterable Python objects containing each exactly 2 elements representing the indices $$(i,j)$$.

Returns

the hessians sparsity patterns

Return type

list of 2D Numpy int array

Raises
• ValueError – if the NumPy arrays returned by the UDP do not satisfy the requirements described above (e.g., invalid shape, dimensions, etc.), at least one element of a returned iterable Python object does not consist of a collection of exactly 2 elements, or if a sparsity pattern returned by the UDP is invalid (specifically, if it is not strictly sorted lexicographically, if the indices in the pattern are incompatible with the properties of the problem or if the size of the pattern differs from the size recorded upon construction)

• OverflowError – if the NumPy arrays returned by the UDP contain integer values which are negative or outside an implementation-defined range

• unspecified – any exception thrown by the underlying C++ function, the PyArray_FROM_OTF() function from the NumPy C API, or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

increment_fevals(n)#

Increment the number of fitness evaluations.

New in version 2.13.

This method will increase the internal counter of fitness evaluations by n.

Parameters

n (int) – the amount by which the internal counter of fitness evaluations will be increased

Raises

unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

is_(t)#

Check the type of the user-defined problem.

This method returns False if extract(t) returns None, and True otherwise.

Parameters

t (type) – the type that will be compared to the type of the UDP

Returns

whether the UDP is of type t or not

Return type

bool

Raises

unspecified – any exception thrown by extract()

is_stochastic()#

Alias for has_set_seed().

set_seed(seed)#

Set the seed for the stochastic variables.

This method will set the seed to be used in the fitness function to instantiate all stochastic variables. If the UDP provides a set_seed() method, then its set_seed() method will be invoked. Otherwise, an error will be raised. The seed parameter must be non-negative.

The set_seed() method of the UDP must be able to take an int as input parameter.

Parameters

seed (int) – the desired seed value

Raises
• NotImplementedError – if the UDP does not provide a set_seed() method

• OverflowError – if seed is negative

• unspecified – any exception raised by the set_seed() method of the UDP or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)
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