Dynamic SC IP Interface

class DynamicSchurComplementInteriorPointInterface(start_t: float, end_t: float, num_time_blocks: int)

Bases: BaseInteriorPointInterface

A class for interfacing with Parapint’s interior point algorithm for the serial solution of dynamic optimization problems. This class is primarily for testing purposes. Users should favor the MPIDynamicSchurComplementInteriorPointInterface class because it supports parallel solution. To utilize this class, create a class which inherits from this class and implement the build_model_for_time_block method. If you override the __init__ method make sure to call the super class’ __init__ method at the end of the derived class’ __init__ method. See ex1.py in the examples directory for an example.

Parameters

start_t: float: The starting time for the dynamic optimization problem
end_t: float: The final time for the dynamic optimization problem
num_time_blocks: int: The number of time blocks to split the time horizon into for parallel solution. This is typically equal to the number of processes available (i.e., comm.Get_size()).

abstract build_model_for_time_block(ndx: int, start_t: float, end_t: float, add_init_conditions: bool) → Tuple[_BlockData, Sequence[_GeneralVarData], Sequence[_GeneralVarData]]

This method should be implemented by derived classes. This method should build (and return) the model for the time interval [start_t, end_t] and return a list of states at start_t and a list of states at end_t (in the same order). This method will be called once for each time block. The start_states and end_states returned by this method must be in the same order for every time block.

Parameters

ndx: int: The time block index
start_t: float
end_t: float
add_init_conditions: bool: This will only be True for time block 0.

Returns

pyomo_model: pyomo.core.base.block.Block: The model for the time interval [start_t, end_t].
start_states: Sequence of _GeneralVarData: a list of the states at start_t; the order of this list should be the same for every time block
end_states: Sequence of _GeneralVarData: a list of the states at end_t; the order of this list should be the same for every time block

n_primals() → int

Returns

n_primals: int: The number of primal variables

primals_lb() → BlockVector

Returns

primals_lb: BlockVector: The lower bounds for each primal variable. This BlockVector has one block for every time block and one block for the coupling variables.

primals_ub() → BlockVector

Returns

primals_ub: BlockVector: The upper bounds for each primal variable. This BlockVector has one block for every time block and one block for the coupling variables.

init_primals() → BlockVector

Returns

init_primals: BlockVector: The initial values for each primal variable. This BlockVector has one block for every time block and one block for the coupling variables.

set_primals(primals: BlockVector)

Set the values of the primal variables for evaluation (i.e., the evaluate_* methods).

Parameters

primals: BlockVector: The values for each primal variable. This BlockVector should have one block for every time block and one block for the coupling variables.

get_primals() → BlockVector

Returns

primals: BlockVector: The values for each primal variable. This BlockVector has one block for every time block and one block for the coupling variables.

evaluate_objective() → float

Returns

objective_val: float: The value of the objective

evaluate_grad_objective() → BlockVector

Returns

grad_obj: BlockVector: The gradient of the objective. This BlockVector has one block for every time block and one block for the coupling variables.

n_eq_constraints() → int

Returns

n_eq_constraints: int: The number of equality constraints, including the coupling constraints

n_ineq_constraints() → int

Returns

n_ineq_constraints: int: The number of inequality constraints

ineq_lb() → BlockVector

Returns

ineq_lb: BlockVector: The lower bounds for each inequality constraint. This BlockVector has one block for every time block.

ineq_ub() → BlockVector

Returns

ineq_lb: BlockVector: The lower bounds for each inequality constraint. This BlockVector has one block for every time block.

init_duals_eq() → BlockVector

Returns

init_duals_eq: BlockVector: The initial values for the duals of the equality constraints, including the coupling constraints. This BlockVector has one block for every time block. Each block is itself a BlockVector with 3 blocks. The first block contains the duals of the equality constraints in the corresponding time block. The second block has the duals for the coupling constraints linking the states at the beginning of the time block to the coupling variables between the time block and the previous time block. The third block has the duals for the coupling constraints linking the states at the end of the time block to the coupling variables between the time block and the next time block.

init_duals_ineq() → BlockVector

Returns

init_duals_ineq: BlockVector: The initial values for the duals of the inequality constraints. This BlockVector has one block for every time block.

set_duals_eq(duals_eq: BlockVector)

Parameters

duals_eq: BlockVector: The values for the duals of the equality constraints, including the coupling constraints. This BlockVector has one block for every time block. Each block is itself a BlockVector with 3 blocks. The first block contains the duals of the equality constraints in the corresponding time block. The second block has the duals for the coupling constraints linking the states at the beginning of the time block to the coupling variables between the time block and the previous time block. The third block has the duals for the coupling constraints linking the states at the end of the time block to the coupling variables between the time block and the next time block.

set_duals_ineq(duals_ineq: BlockVector)

Parameters

duals_ineq: BlockVector: The values for the duals of the inequality constraints. This BlockVector has one block for every time block.

get_duals_eq() → BlockVector

Returns

duals_eq: BlockVector: The values for the duals of the equality constraints, including the coupling constraints. This BlockVector has one block for every time block. Each block is itself a BlockVector with 3 blocks. The first block contains the duals of the equality constraints in the corresponding time block. The second block has the duals for the coupling constraints linking the states at the beginning of the time block to the coupling variables between the time block and the previous time block. The third block has the duals for the coupling constraints linking the states at the end of the time block to the coupling variables between the time block and the next time block.

get_duals_ineq() → BlockVector

Returns

duals_ineq: BlockVector: The values for the duals of the inequality constraints. This BlockVector has one block for every time block.

evaluate_eq_constraints() → BlockVector

Returns

eq_resid: BlockVector: The residuals of the equality constraints, including the coupling constraints. This BlockVector has one block for every time block. Each block is itself a BlockVector with 3 blocks. The first block contains the residuals of the equality constraints in the corresponding time block. The second block has the residuals for the coupling constraints linking the states at the beginning of the time block to the coupling variables between the time block and the previous time block. The third block has the residuals for the coupling constraints linking the states at the end of the time block to the coupling variables between the time block and the next time block.

evaluate_ineq_constraints() → BlockVector

Returns

ineq_resid: BlockVector: The residuals of the inequality constraints. This BlockVector has one block for every time block.

evaluate_jacobian_eq() → BlockMatrix

Returns

jac_eq: BlockMatrix: The jacobian of the equality constraints. The rows have the same structure as the BlockVector returned from evaluate_eq_constraints. The columns have the same structure as the BlockVector returned from get_primals.

evaluate_jacobian_ineq() → BlockMatrix

Returns

jac_ineq: BlockMatrix: The jacobian of the inequality constraints. The rows have the same structure as the BlockVector returned from evaluate_ineq_constraints. The columns have the same structure as the BlockVector returned from get_primals.

load_primals_into_pyomo_model(): This method takes the current values for the primal variables (those you would get from the get_primals() method), and loads them into the corresponding Pyomo variables.

pyomo_model(ndx: int) → _BlockData

Parameters

ndx: int: The index of the time block for which the pyomo model should be returned.

Returns

m: _BlockData: The pyomo model for the time block corresponding to ndx.

get_pyomo_variables(ndx: int) → Sequence[_GeneralVarData]

Parameters

ndx: int: The index of the time block for which pyomo variables should be returned

Returns

pyomo_vars: list of _GeneralVarData: The pyomo variables in the model for the time block corresponding to ndx

get_pyomo_constraints(ndx: int) → Sequence[_GeneralConstraintData]

Parameters

ndx: int: The index of the time block for which pyomo constraints should be returned

Returns

pyomo_cons: list of _GeneralConstraintData: The pyomo constraints in the model for the time block corresponding to ndx

get_primal_indices(ndx: int, pyomo_variables: Sequence[_GeneralVarData]) → Sequence[int]

Parameters

ndx: int: The index of the time block
pyomo_variables: Sequence of _GeneralVarData: The pyomo variables for which the indices should be returned

Returns

var_indices: Sequence of int: The indices of the corresponding pyomo variables. Note that these indices correspond to the specified time block, not the overall indices. In other words, the indices that are returned are the indices into the block within get_primals corresponding to ndx.

get_constraint_indices(ndx, pyomo_constraints) → Sequence[int]

Parameters

ndx: int: The index of the time block
pyomo_constraints: Sequence of _GeneralConstraintData: The pyomo constraints for which the indices should be returned

Returns

con_indices: Sequence of int: The indices of the corresponding pyomo constraints. Note that these indices correspond to the specified time block, not the overall indices.