pyepo.model.mpax.mpaxmodel

Abstract optimization model based on MPAX

Attributes

`_HAS_MPAX`
`num_vars`

Classes

optMpaxModel

This is an abstract class for MPAX-based optimization model

Module Contents

pyepo.model.mpax.mpaxmodel._HAS_MPAX = True

class pyepo.model.mpax.mpaxmodel.optMpaxModel(A=None, b=None, G=None, h=None, l=None, u=None, use_sparse_matrix=True, minimize=True)

Bases: pyepo.model.opt.optModel

This is an abstract class for MPAX-based optimization model

A

The matrix of equality constraints.

Type:: jnp.ndarray, BCOO or BCSR

b

The right hand side of equality constraints.

Type:: jnp.ndarray

G

The matrix for inequality constraints.

Type:: jnp.ndarray, BCOO or BCSR

h

The right hand side of inequality constraints.

Type:: jnp.ndarray

l

The lower bound of the variables.

Type:: jnp.ndarray

u

The upper bound of the variables.

Type:: jnp.ndarray

use_sparse_matrix

Whether to use sparse matrix format, by default True.

Type:: bool

minimize

Whether to minimize objective, by default True.

Type:: bool

l

u

use_sparse_matrix = True

modelSense = 1

device = None

jitted_solve = None

batch_optimize

__repr__()

property num_cost: number of cost to be predicted

setObj(c)

A method to set objective function

Parameters:: c (np.ndarray / list) – cost of objective function

solve()

A method to solve model

Returns:: optimal solution (list) and objective value (jnp.float32)
Return type:: tuple

static _jitted_solve(c, A, b, G, h, l, u, use_sparse_matrix): A static method for JIT complile

copy()

A method to copy model

Returns:: new copied model
Return type:: optModel

addConstr(coefs, rhs)

A method to add new constraint

Parameters:

coefs (np.ndarray / list) – coeffcients of new constraint
rhs (jnp.float32) – right-hand side of new constraint

Returns:

new model with the added constraint

Return type:

optModel

_getModel(): Placeholder method for MPAX. MPAX does not require an explicit model creation.

pyepo.model.mpax.mpaxmodel.num_vars = 10