pyepo.model.mpax

Optimization Model based on MPAX

Submodules

Classes

`optMpaxModel`	This is an abstract class for MPAX-based optimization model
`shortestPathModel`	This class is optimization model for shortest path problem
`knapsackModel`	This class is optimization model for relexed knapsack problem

Package Contents

class pyepo.model.mpax.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.

class pyepo.model.mpax.shortestPathModel(grid)

Bases: pyepo.model.mpax.mpaxmodel.optMpaxModel

This class is optimization model for shortest path problem

grid

Size of grid network

Type:: tuple of int

arcs

List of arcs

Type:: list

grid

arcs = []

_getArcs()

A method to get list of arcs for grid network

Returns:: arcs
Return type:: list

_constructMatrix()

Constructs the incidence matrix A, supply/demand vector b, and upper bound u for the shortest path problem.

Returns:: Incidence matrix for flow conservation b (jnp.ndarray): Supply/demand vector u (jnp.ndarray): Upper bound for flow variables
Return type:: A (jnp.ndarray)

class pyepo.model.mpax.knapsackModel(weights, capacity)

Bases: pyepo.model.mpax.mpaxmodel.optMpaxModel

This class is optimization model for relexed knapsack problem

_model

Gurobi model

Type:: GurobiPy model

weights

Weights of items

Type:: np.ndarray / list

capacity

Total capacity

Type:: np.ndarray / listy

items

List of item index

Type:: list

weights

capacity

items

_constructMatrix()

Constructs the inequality constraint matrix G, right-hand side h, and upper bound u for the knapsack problem.

Returns:: Weights of items h (jnp.ndarray): Total capacity u (jnp.ndarray): Upper bound for item selection
Return type:: G (jnp.ndarray)