optas.optimization.Optimization Class Reference

Base optimization class. More...

Inheritance diagram for optas.optimization.Optimization:

Public Member Functions
def	__init__ (self, SXContainer decision_variables, SXContainer parameters, SXContainer cost_terms)
	Initializer for the Optimization class. More...
None	set_models (self, List[Model] models)
	Specify the models in the optimization problem. More...
None	specify_quadratic_cost (self)
	Specify the terms P and q of a quadratic cost function. More...
None	specify_linear_constraints (self, lin_ineq_constraints, lin_eq_constraints)
	Setup the constraints k(x, p) = M(p).x + c(p) >= 0, and a(x, p) = A(p).x + b(p) == 0. More...
None	specify_nonlinear_constraints (self, SXContainer ineq_constraints, SXContainer eq_constraints)
	Setup the constraints g(x, p) >= 0, and h(x, p) == 0. More...
None	specify_v (self, List[cs.Function] ineq=[], List[cs.Function] eq=[])
	Specify the vertical constraints vector v. More...
def	has_discrete_variables (self)

Public Attributes
	models
	A list of the task and robot models (set during build method in the OptimizationBuilder class) More...
	decision_variables
	SXContainer containing decision variables. More...
	parameters
	SXContainer containing parameters. More...
	cost_terms
	SXContainer containing cost terms. More...
	lin_eq_constraints
	SXContainer containing linear equality constraints. More...
	lin_ineq_constraints
	SXContainer containing linear inequality constraints. More...
	eq_constraints
	SXContainer containing equality constraints. More...
	ineq_constraints
	SXContainer containing inequality constraints. More...
	P
	CasADi function that evaluates the P term in the cost function (note, this only applies to problems with a quadratic cost function). More...
	q
	CasADi function that evaluates the q term in the cost function (note, this only applies to problems with a quadratic cost function). More...
	k
	CasADi function that evaluates the linear equality constraints. More...
	nk
	Number of linear inequality constraints. More...
	lbk
	Lower bound for the linear inequality constraints (i.e. More...
	ubk
	Upper bound for the linear inequality constraints (i.e. More...
	M
	CasADi function that evaluates the M term in the linear inequality constraints. More...
	c
	CasADi function that evaluates the c term in the linear inequality constraints. More...
	a
	CasADi function that evaluates the linear equality constraints. More...
	na
	Number of linear equality constraints. More...
	lba
	Lower bound for the linear equality constraints (i.e. More...
	uba
	Upper bound for the linear equality constraints (i.e. More...
	A
	CasADi function that evaluates the A term in the linear equality constraints. More...
	b
	CasADi function that evaluates the b term in the linear equality constraints. More...
	g
	CasADi function that evaluates the inequality constraints. More...
	ng
	Number of inequality constraints. More...
	lbg
	Lower bound for the inequality constraints (i.e. More...
	ubg
	Upper bound for the inequality constraints (i.e. More...
	h
	CasADi function that evaluates the equality constraints. More...
	nh
	Number of equality constraints. More...
	lbh
	Lower bound for the equality constraints (i.e. More...
	ubh
	Upper bound for the equality constraints (i.e. More...
	v
	CasADi function that evaluates the constraints as a verticle column (set when specify_v is called), see vertcon. More...
	nv
	Number of vectorized constraints (see vertcon). More...
	lbv
	Lower bound for the verticle constraints v (i.e. More...
	ubv
	Upper bound for the verticle constraints v (i.e. More...
	dv
	CasADi function that evaluates the Jacobian of the constraints v (set when specify_v is called). More...
	ddv
	CasADi function that evaluates the Hessian of the constraints v (set when specify_v is called). More...
	x
	Vectorized decision variables. More...
	p
	Vectorized parameters. More...
	f
	CasADi function that evaluates the objective function. More...
	df
	Jacobian of the objective function. More...
	ddf
	Hessian of the objective function. More...
	nx
	Number of decision variables. More...
	np
	Number of parameters. More...
	ddg
	ddh

Static Public Attributes
float	inf = 1.0e10
	big number rather than np.inf More...

Detailed Description

Base optimization class.

Constructor & Destructor Documentation

__init__()

def optas.optimization.Optimization.__init__	(		self,
		SXContainer	decision_variables,
		SXContainer	parameters,
		SXContainer	cost_terms
	)

Initializer for the Optimization class.

   @param decision_variables SXContainer containing decision variables.
   @param parameters SXContainer containing parameters.
   @param cost_terms SXContainer containing cost terms.
   @return Instance of the Optimization class.

Reimplemented in optas.optimization.NonlinearCostUnconstrained, and optas.optimization.QuadraticCostUnconstrained.

Member Function Documentation

has_discrete_variables()

def optas.optimization.Optimization.has_discrete_variables

(

self

)

set_models()

None optas.optimization.Optimization.set_models	(		self,
		List[Model]	models
	)

Specify the models in the optimization problem.

   @param models A list of models (i.e. instance of a sub-class of Model).

specify_linear_constraints()

None optas.optimization.Optimization.specify_linear_constraints	(		self,
			lin_ineq_constraints,
			lin_eq_constraints
	)

Setup the constraints k(x, p) = M(p).x + c(p) >= 0, and a(x, p) = A(p).x + b(p) == 0.

   @param lin_ineq_constraints SXContainer containing the linear inequality constraints.
   @param lin_eq_constraints SXContainer containing the linear equality constraints.

specify_nonlinear_constraints()

None optas.optimization.Optimization.specify_nonlinear_constraints	(		self,
		SXContainer	ineq_constraints,
		SXContainer	eq_constraints
	)

Setup the constraints g(x, p) >= 0, and h(x, p) == 0.

   @param ineq_constraints SXContainer containing the inequality constraints.
   @param eq_constraints SXContainer containing the equality constraints.

specify_quadratic_cost()

None optas.optimization.Optimization.specify_quadratic_cost

(

self

)

Specify the terms P and q of a quadratic cost function.

specify_v()

None optas.optimization.Optimization.specify_v	(		self,
		List[cs.Function]	ineq = [],
		List[cs.Function]	eq = []
	)

Specify the vertical constraints vector v.

This is only called for optimization problems that have constraints.

   @param ineq List of CasADi functions that evalaute the (non)linear inequality constraints.
   @param ineq List of CasADi functions that evalaute the (non)linear equality constraints.

Member Data Documentation

a

optas.optimization.Optimization.a

CasADi function that evaluates the linear equality constraints.

A

optas.optimization.Optimization.A

CasADi function that evaluates the A term in the linear equality constraints.

b

optas.optimization.Optimization.b

CasADi function that evaluates the b term in the linear equality constraints.

c

optas.optimization.Optimization.c

CasADi function that evaluates the c term in the linear inequality constraints.

cost_terms

optas.optimization.Optimization.cost_terms

SXContainer containing cost terms.

ddf

optas.optimization.Optimization.ddf

Hessian of the objective function.

ddg

optas.optimization.Optimization.ddg

ddh

optas.optimization.Optimization.ddh

ddv

optas.optimization.Optimization.ddv

CasADi function that evaluates the Hessian of the constraints v (set when specify_v is called).

decision_variables

optas.optimization.Optimization.decision_variables

SXContainer containing decision variables.

df

optas.optimization.Optimization.df

Jacobian of the objective function.

dv

optas.optimization.Optimization.dv

CasADi function that evaluates the Jacobian of the constraints v (set when specify_v is called).

eq_constraints

optas.optimization.Optimization.eq_constraints

SXContainer containing equality constraints.

f

optas.optimization.Optimization.f

CasADi function that evaluates the objective function.

g

optas.optimization.Optimization.g

CasADi function that evaluates the inequality constraints.

h

optas.optimization.Optimization.h

CasADi function that evaluates the equality constraints.

ineq_constraints

optas.optimization.Optimization.ineq_constraints

SXContainer containing inequality constraints.

inf

float optas.optimization.Optimization.inf = 1.0e10

static

big number rather than np.inf

k

optas.optimization.Optimization.k

CasADi function that evaluates the linear equality constraints.

lba

optas.optimization.Optimization.lba

Lower bound for the linear equality constraints (i.e.

zeros).

lbg

optas.optimization.Optimization.lbg

Lower bound for the inequality constraints (i.e.

zeros).

lbh

optas.optimization.Optimization.lbh

Lower bound for the equality constraints (i.e.

zeros).

lbk

optas.optimization.Optimization.lbk

Lower bound for the linear inequality constraints (i.e.

zeros).

lbv

optas.optimization.Optimization.lbv

Lower bound for the verticle constraints v (i.e.

zeros).

lin_eq_constraints

optas.optimization.Optimization.lin_eq_constraints

SXContainer containing linear equality constraints.

lin_ineq_constraints

optas.optimization.Optimization.lin_ineq_constraints

SXContainer containing linear inequality constraints.

M

optas.optimization.Optimization.M

CasADi function that evaluates the M term in the linear inequality constraints.

models

optas.optimization.Optimization.models

A list of the task and robot models (set during build method in the OptimizationBuilder class)

na

optas.optimization.Optimization.na

Number of linear equality constraints.

ng

optas.optimization.Optimization.ng

Number of inequality constraints.

nh

optas.optimization.Optimization.nh

Number of equality constraints.

nk

optas.optimization.Optimization.nk

Number of linear inequality constraints.

np

optas.optimization.Optimization.np

Number of parameters.

nv

optas.optimization.Optimization.nv

Number of vectorized constraints (see vertcon).

nx

optas.optimization.Optimization.nx

Number of decision variables.

P

optas.optimization.Optimization.P

CasADi function that evaluates the P term in the cost function (note, this only applies to problems with a quadratic cost function).

p

optas.optimization.Optimization.p

Vectorized parameters.

parameters

optas.optimization.Optimization.parameters

SXContainer containing parameters.

q

optas.optimization.Optimization.q

CasADi function that evaluates the q term in the cost function (note, this only applies to problems with a quadratic cost function).

uba

optas.optimization.Optimization.uba

Upper bound for the linear equality constraints (i.e.

zeros).

ubg

optas.optimization.Optimization.ubg

Upper bound for the inequality constraints (i.e.

inf).

ubh

optas.optimization.Optimization.ubh

Upper bound for the equality constraints (i.e.

zeros).

ubk

optas.optimization.Optimization.ubk

Upper bound for the linear inequality constraints (i.e.

inf).

ubv

optas.optimization.Optimization.ubv

Upper bound for the verticle constraints v (i.e.

inf).

v

optas.optimization.Optimization.v

CasADi function that evaluates the constraints as a verticle column (set when specify_v is called), see vertcon.

x

optas.optimization.Optimization.x

Vectorized decision variables.

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

• optimization.py