Parameters

A parameter behaves like a fixed variable inside an expression, and its value can be changed afterwards without rebuilding the model — the derivative code is unaffected.

import examodels as exa

N = 10
core = exa.Core()
th = exa.add_par(core, [100.0, 1.0])
x = exa.add_var(core, N, start=1.0)

exa.add_obj(core, lambda i: th[0] * (x[i-1]**2 - x[i])**2 + (x[i-1] - th[1])**2,
            over=range(1, N))

model = exa.Model(core)
first = model.solve()

model.set_value(th, [200.0, 1.0])
second = model.solve()                 # same model, new values

get_value reads them back. On a device model the values are placed on the device; nothing about the call changes.

Reading and changing a built model

Every one of the backend’s accessors is available, on variable, parameter and constraint blocks:

read	change
get_value	set_value
get_start	set_start
get_lvar, get_uvar	set_lvar, set_uvar
get_lcon, get_ucon	set_lcon, set_ucon

All of them work in place, and values are given and returned in the shape you used — a multi-dimensional block is never handed back transposed or flattened.