optas.solver.ScipyMinimizeSolver Class Reference

Inheritance diagram for optas.solver.ScipyMinimizeSolver:

Collaboration diagram for optas.solver.ScipyMinimizeSolver:

Public Member Functions
def	setup (self, str method="SLSQP", Union[None, float] tol=None, Union[None, Dict] options=None)
cs.np.ndarray	f (self, cs.np.ndarray x)
	Internal method. More...
cs.np.ndarray	jac (self, cs.np.ndarray x)
	Internal method. More...
cs.np.ndarray	hess (self, cs.np.ndarray x)
	Internal method. More...
cs.np.ndarray	v (self, cs.np.ndarray x)
	Internal method. More...
cs.np.ndarray	dv (self, cs.np.ndarray x)
	Internal method. More...
cs.np.ndarray	g (self, cs.np.ndarray x)
	Internal method. More...
cs.np.ndarray	dg (self, cs.np.ndarray x)
	Internal method. More...
cs.np.ndarray	ddg (self, cs.np.ndarray x)
	Internal method. More...
cs.np.ndarray	h (self, cs.np.ndarray x)
	Internal method. More...
cs.np.ndarray	dh (self, cs.np.ndarray x)
	Internal method. More...
cs.np.ndarray	ddh (self, cs.np.ndarray x)
	Internal method. More...
None	reset_initial_seed (self, x0)
	Reset initial seed for the optimization problem. More...
def	reset_parameters (self, Dict[str, ArrayType] p)
	Reset the parameters. More...
def	stats (self)
	Statistics relating to the previous call to solve. More...
def	did_solve (self)
	Returns True when the problem was solved. More...
def	number_of_iterations (self)
	Number of iterations it took the solver to converge. More...
Public Member Functions inherited from optas.solver.Solver
def	__init__ (self, Optimization optimization, bool error_on_fail=False)
	Constructor for the base Solver class. More...
type	opt_type (self)
	Optimization type. More...
def	setup (self, *args, **kwargs)
	Setup solver, note this method must return self. More...
None	reset_initial_seed (self, Dict[str, ArrayType] x0)
	Reset initial seed for the optimization problem. More...
Dict	solve (self)
	Solve the optimization problem. More...
Tuple	violated_constraints (self, Dict[str, ArrayType] x, Dict[str, ArrayType] p)
	Indicate the violated constraints. More...
CasADiArrayType	evaluate_cost (self, Dict[str, ArrayType] x, Dict[str, ArrayType] p)
	Evaluates the cost function for given decision variables x and parameters p. More...
List	evaluate_cost_terms (self, Dict[str, ArrayType] x, Dict[str, ArrayType] p)
	Evaluates each cost term for given decision variables and parameters. More...

Public Attributes
	method
	Method name. More...
	minimize_input
	Input to the minimize method. More...
Public Attributes inherited from optas.solver.Solver
	opt
	Instance of the optimization problem. More...
	x0
	Initial guess for the optimization problem (set using reset_initial_seed). More...
	p
	Parameter vector. More...

Static Public Attributes
dictionary	methods_req_jac
	Methods that require the Jacobian of the objective. More...
dictionary	methods_req_hess
	Methods that require the Hessian of the objective. More...
dictionary	methods_handle_constraints = {"COBYLA", "SLSQP", "trust-constr"}
	Methods that handle constrained optimization problems. More...

Private Member Functions
CasADiArrayType	_solve (self)
	Solve the optimization problem using Scipy. More...

Private Attributes
	_stats
	Container for the statistics. More...
	_constraints
	Constraints definition passed to the minimize method. More...
	_solution

Additional Inherited Members
Static Public Member Functions inherited from optas.solver.Solver
interp1d	interpolate (cs.DM traj, float T, **interp_args)
	Interpolate a trajectory. More...

Detailed Description

Scipy solver (scipy.optimize.minimize) interface.

Member Function Documentation

_solve()

CasADiArrayType optas.solver.ScipyMinimizeSolver._solve (   self )

private

Solve the optimization problem using Scipy.

   @return The solution of the optimization problem.

Reimplemented from optas.solver.Solver.

ddg()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.ddg	(		self,
		cs.np.ndarray	x
	)

Internal method.

ddh()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.ddh	(		self,
		cs.np.ndarray	x
	)

Internal method.

dg()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.dg	(		self,
		cs.np.ndarray	x
	)

Internal method.

dh()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.dh	(		self,
		cs.np.ndarray	x
	)

Internal method.

did_solve()

def optas.solver.ScipyMinimizeSolver.did_solve

(

self

)

Returns True when the problem was solved.

   @return Boolean indicating if the solver converged.

Reimplemented from optas.solver.Solver.

dv()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.dv	(		self,
		cs.np.ndarray	x
	)

Internal method.

f()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.f	(		self,
		cs.np.ndarray	x
	)

Internal method.

g()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.g	(		self,
		cs.np.ndarray	x
	)

Internal method.

h()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.h	(		self,
		cs.np.ndarray	x
	)

Internal method.

hess()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.hess	(		self,
		cs.np.ndarray	x
	)

Internal method.

jac()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.jac	(		self,
		cs.np.ndarray	x
	)

Internal method.

number_of_iterations()

def optas.solver.ScipyMinimizeSolver.number_of_iterations

(

self

)

Number of iterations it took the solver to converge.

   @return Number of iterations.

Reimplemented from optas.solver.Solver.

reset_initial_seed()

None optas.solver.ScipyMinimizeSolver.reset_initial_seed	(		self,
			x0
	)

Reset initial seed for the optimization problem.

   @param x0 The initial seed.

reset_parameters()

def optas.solver.ScipyMinimizeSolver.reset_parameters	(		self,
		Dict[str, ArrayType]	p
	)

Reset the parameters.

   @param p The values for the parameters.

Reimplemented from optas.solver.Solver.

setup()

def optas.solver.ScipyMinimizeSolver.setup	(		self,
		str	method = "SLSQP",
		Union[None, float]	tol = None,
		Union[None, Dict]	options = None
	)

Setup the Scipy solver.

@param method Type of solver. Default is "SLSQP".
@param tol Tolerance for termination. When tol is specified, the selected minimization algorithm sets some relevant solver-specific tolerance(s) equal to tol. For detailed control, use solver-specific options.
@param options A dictionary of solver options.
@return The instance of the solve (i.e. self).

stats()

def optas.solver.ScipyMinimizeSolver.stats

(

self

)

Statistics relating to the previous call to solve.

   @return Dictionary containing the statistics.

Reimplemented from optas.solver.Solver.

v()

cs.np.ndarray optas.solver.ScipyMinimizeSolver.v	(		self,
		cs.np.ndarray	x
	)

Internal method.

Member Data Documentation

_constraints

optas.solver.ScipyMinimizeSolver._constraints

private

Constraints definition passed to the minimize method.

_solution

optas.solver.ScipyMinimizeSolver._solution

private

_stats

optas.solver.ScipyMinimizeSolver._stats

private

Container for the statistics.

method

optas.solver.ScipyMinimizeSolver.method

Method name.

methods_handle_constraints

dictionary optas.solver.ScipyMinimizeSolver.methods_handle_constraints = {"COBYLA", "SLSQP", "trust-constr"}

static

Methods that handle constrained optimization problems.

methods_req_hess

dictionary optas.solver.ScipyMinimizeSolver.methods_req_hess

static

Initial value:

=  {
        "Newton-CG",
        "dogleg",
        "trust-ncg",
        "trust-krylov",
        "trust-exact",
        "trust-constr",
    }

Methods that require the Hessian of the objective.

methods_req_jac

dictionary optas.solver.ScipyMinimizeSolver.methods_req_jac

static

Initial value:

=  {
        "CG",
        "BFGS",
        "Newton-CG",
        "L-BFGS-B",
        "TNC",
        "SLSQP",
        "dogleg",
        "trust-ncg",
        "trust-krylov",
        "trust-exact",
        "trust-constr",
    }

Methods that require the Jacobian of the objective.

minimize_input

optas.solver.ScipyMinimizeSolver.minimize_input

Input to the minimize method.

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The documentation for this class was generated from the following file:

• solver.py