optas.solver.Solver Class Reference

Solver base class. More...

Inheritance diagram for optas.solver.Solver:

Collaboration diagram for optas.solver.Solver:

Public Member Functions
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...
None	reset_parameters (self, Dict[str, ArrayType] p)
	Reset the parameters for the optimization problem. More...
Dict	solve (self)
	Solve the optimization problem. More...
def	stats (self)
	Return stats from solver. More...
Tuple	violated_constraints (self, Dict[str, ArrayType] x, Dict[str, ArrayType] p)
	Indicate the violated constraints. More...
bool	did_solve (self)
	Returns true when the solver solved the previous problem, false otherwise. More...
int	number_of_iterations (self)
	Returns the number of iterations required to solve the problem. 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...

Static Public Member Functions
interp1d	interpolate (cs.DM traj, float T, **interp_args)
	Interpolate a trajectory. More...

Public Attributes
	opt
	Instance of the optimization problem. More...
	x0
	Initial guess for the optimization problem (set using reset_initial_seed). More...
	p
	Parameter vector. More...

Private Member Functions
CasADiArrayType	_solve (self)
	Solve the optimization problem and return the optimal decision variables as an array. More...

Private Attributes
	_p_dict
	Parameter dictionary. More...
	_error_on_fail
	When True, after solve() is called, if the solver did not converge then a RuntimeError is thrown. More...
	_solution
	Solution container. More...

Detailed Description

Solver base class.

Base solver interface class

Constructor & Destructor Documentation

__init__()

def optas.solver.Solver.__init__	(		self,
		Optimization	optimization,
		bool	error_on_fail = False
	)

Constructor for the base Solver class.

   @param optimization The optimization problem created by calling the build method of the OptimizationBuilder class.
   @param error_on_fail When True, after solve() is called, if the solver did not converge then a RuntimeError is thrown. Default is False.
   @return An instance of the Solver class.

Member Function Documentation

_solve()

CasADiArrayType optas.solver.Solver._solve (   self )

private

Solve the optimization problem and return the optimal decision variables as an array.

This is an abstract method.

   @return The solution from the solver.

Reimplemented in optas.solver.ScipyMinimizeSolver, optas.solver.CVXOPTSolver, optas.solver.OSQPSolver, and optas.solver.CasADiSolver.

did_solve()

bool optas.solver.Solver.did_solve

(

self

)

Returns true when the solver solved the previous problem, false otherwise.

This is an abstract method.

   @return Result of whether the solver succeeded or not.

Reimplemented in optas.solver.ScipyMinimizeSolver, optas.solver.CVXOPTSolver, optas.solver.OSQPSolver, and optas.solver.CasADiSolver.

evaluate_cost()

CasADiArrayType optas.solver.Solver.evaluate_cost	(		self,
		Dict[str, ArrayType]	x,
		Dict[str, ArrayType]	p
	)

Evaluates the cost function for given decision variables x and parameters p.

   @param x The values for the decision variables.
   @param p The values for the parameters.
   @return The cost that results from x and p.

evaluate_cost_terms()

List optas.solver.Solver.evaluate_cost_terms	(		self,
		Dict[str, ArrayType]	x,
		Dict[str, ArrayType]	p
	)

Evaluates each cost term for given decision variables and parameters.

   @param x The values for the decision variables.
   @param p The values for the parameters.
   @return List corresponding to each cost term evalauted at x, p.

interpolate()

interp1d optas.solver.Solver.interpolate ( cs.DM  traj, float  T, **  interp_args  )

static

Interpolate a trajectory.

   @param traj The trajectory to be interpolated where the columns correspond to the states over time.
   @param T The time duration of the trajectory.
   @return An interpolated function.

number_of_iterations()

int optas.solver.Solver.number_of_iterations

(

self

)

Returns the number of iterations required to solve the problem.

This is an abstract method.

   @return Number of iterations.

Reimplemented in optas.solver.ScipyMinimizeSolver, optas.solver.CVXOPTSolver, optas.solver.OSQPSolver, and optas.solver.CasADiSolver.

opt_type()

type optas.solver.Solver.opt_type

(

self

)

Optimization type.

   @return The type of the optimization problem.

reset_initial_seed()

None optas.solver.Solver.reset_initial_seed	(		self,
		Dict[str, ArrayType]	x0
	)

Reset initial seed for the optimization problem.

   @param x0 The initial seed.

reset_parameters()

None optas.solver.Solver.reset_parameters	(		self,
		Dict[str, ArrayType]	p
	)

Reset the parameters for the optimization problem.

   @param p Specifies the parameters.

Reimplemented in optas.solver.ScipyMinimizeSolver, optas.solver.CVXOPTSolver, and optas.solver.OSQPSolver.

setup()

def optas.solver.Solver.setup	(		self,
		*	args,
		**	kwargs
	)

Setup solver, note this method must return self.

This is an abstract method.

solve()

Dict optas.solver.Solver.solve

(

self

)

Solve the optimization problem.

   @return A dictionary containing the solution.

stats()

def optas.solver.Solver.stats

(

self

)

Return stats from solver.

The return type is specifc to the solver used. This is an abstract method.

   @return The statistics returned by the solver. This is specified to the solver used.

Reimplemented in optas.solver.ScipyMinimizeSolver, optas.solver.CVXOPTSolver, optas.solver.OSQPSolver, and optas.solver.CasADiSolver.

violated_constraints()

Tuple optas.solver.Solver.violated_constraints	(		self,
		Dict[str, ArrayType]	x,
		Dict[str, ArrayType]	p
	)

Indicate the violated constraints.

   @param x The values for the decision variables.
   @param p The values for the parameters.
   @return Several lists that contain information regarding which constraints are violated.

Member Data Documentation

_error_on_fail

optas.solver.Solver._error_on_fail

private

When True, after solve() is called, if the solver did not converge then a RuntimeError is thrown.

_p_dict

optas.solver.Solver._p_dict

private

Parameter dictionary.

_solution

optas.solver.Solver._solution

private

Solution container.

opt

optas.solver.Solver.opt

Instance of the optimization problem.

p

optas.solver.Solver.p

Parameter vector.

x0

optas.solver.Solver.x0

Initial guess for the optimization problem (set using reset_initial_seed).

---

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

• solver.py