Nonlinear Models#
This page describes the dwaveoptimization package’s nonlinear model: classes, attributes, and methods.
For examples, see Ocean’s Getting Started examples.
Nonlinear models are especially suited for use with decision variables that represent a common logic, such as subsets of choices or permutations of ordering. For example, in a traveling salesperson problem permutations of the variables representing cities can signify the order of the route being optimized and in a knapsack problem the variables representing items can be divided into subsets of packed and not packed.
Model Class#
 class Model#
Nonlinear model.
The nonlinear model represents a general optimization problem with an objective function and/or constraints over variables of various types.
The
Model
class can contain this model and its methods provide convenient utilities for working with representations of a problem.Examples
This example creates a model for a
flowshopscheduling
problem with two jobs on three machines.>>> from dwave.optimization.generators import flow_shop_scheduling ... >>> processing_times = [[10, 5, 7], [20, 10, 15]] >>> model = flow_shop_scheduling(processing_times=processing_times)
Model Attributes#
Objective to be minimized. 

States of the model. 
Model Primitives#
Instantiation of the model’s decision (and constant) symbols. For the full list of supported symbols, see the Symbols page.

Create a binary symbol as a decision variable. 

Create a constant symbol. 

Create a disjointsets symbol as a decision variable. 

Create a disjointlists symbol as a decision variable. 

Create an integer symbol as a decision variable. 

Create a list symbol as a decision variable. 

Create a set symbol as a decision variable. 
Model Methods#

Add a constraint to the model. 
An estimated size, in bytes, of the model's decision states. 


Construct a model from the given file. 

Serialize the model into an existing file. 
Lock status of the model. 

Iterate over all constraints in the model. 

Iterate over all decision variables in the model. 

Iterate over all symbols in the model. 


Lock the model. 

Set the objective value to minimize. 
Number of constraints in the model. 

Number of independent decision nodes in the model. 

Number of nodes in the directed acyclic graph for the model. 

Number of symbols tracked by the model. 


Create a quadratic model from an array and a quadratic model. 
An estimate of the size, in bytes, of all states in the model. 


Serialize the model to a new filelike object. 
Convert the model to a NetworkX graph. 


Release a lock, decrementing the lock count. 
States Class#
 class States#
States of a symbol in a model.
States represent assignments of values to a symbol’s elements. For example, an
integer()
symbol of size \(1 \times 5\) might have state[3, 8, 0, 12, 8]
, representing one assignment of values to the symbol.Examples
This example creates a
knapsack
model and manipulates its states to test that it behaves as expected.First, create a model.
>>> from dwave.optimization import Model ... >>> model = Model() >>> # Add constants >>> weights = model.constant([10, 20, 5, 15]) >>> values = model.constant([5, 7, 2, 9]) >>> capacity = model.constant(30) >>> # Add the decision variable >>> items = model.set(4) >>> # add the capacity constraint >>> model.add_constraint(weights[items].sum() <= capacity) >>> # Set the objective >>> model.minimize(values[items].sum())
Lock the model to prevent changes to directed acyclic graph. At any time, you can verify the locked state, which is demonstrated here.
>>> with model.lock(): ... model.is_locked() True
Set a couple of states on the decision variable and verify that the model generates the expected values for the objective.
>>> model.states.resize(2) >>> items.set_state(0, [0, 1]) >>> items.set_state(1, [0, 2, 3]) >>> with model.lock(): ... print(model.objective.state(0) > model.objective.state(1)) True
You can clear the states you set.
>>> model.states.clear() >>> model.states.size() 0
States Methods#

Clear any saved states. 

Construct states from the given file. 

Populate the states from the result of a future computation. 

Serialize the states into an existing file. 

Resize the number of states. 

Block until states are retrieved from any pending future computations. 

Number of model states. 

Serialize the states to a new filelike object. 