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The Knapsack problem

Original The Knapsack Problem by Saad Attieh and Christopher Stone. Adapted by Alex Gallagher.

The Knapsack problem is a classical combinatorial optimisation problem, often used in areas of resource allocation. A basic variant of the Knapsack problem is defined as follows:

Given: 1. A set of items, each with a weight and a value, 2. A maximum weight which we call capacity,

find a set of the items such that 1. The sum of the weights of the items in our set is less than or equal to the capacity, and 2. The sum of the values of the items is maximised.

Informally, think about putting items in a sack such that we maximise the total value of the sack whilst not going over the sack’s weight limit.

!source <(curl -s
%load_ext conjure

Installing Conjure version v2.5.1 and Conjure Notebook version v0.0.9...
Conjure: The Automated Constraint Modelling Tool
Release version 2.5.0
Repository version c94cde5 (2023-11-06 17:43:12 +0000)
Conjure extension is loaded.
For usage help run: %conjure_help

##Model Explained

We begin by showing the entire problem as defined in Essence:

given items new type enum

given weight : function (total) items --> int

given gain : function (total) items --> int

given capacity : int

find picked : set of items

maximising sum i in picked . gain(i)

such that (sum i in picked . weight(i)) <= capacity

Going through the problem line by line:

We begin by defining the parameters to the problem. Parameters can be defined outside of the problem, allowing different instances of the same problem to be solved without having to change the specification.

Each parameter is denoted with the given keyword.

given items new type enum

This line says that a set of items will be provided as an enum type. Enums are good for labeling items where it makes no sense to attribute a value to each item. So instead of using integers to represent each item, we may just assign names to each item and group the names under an enum type. Below is an example enum declaration, as it would be written in the Conjure Magic cell:

letting items be new type enum {a,b,c,d,e}

a, b, etc. are just names we have given, they could be anything bread, whiskey, …

given weight : function (total) items --> int

Another parameter, a function that maps from each item to an integer, we will treat these integers as weights. Since we are describing integers that will be given as parameters, no domain (lower/upper bound) is required. Here is an example function parameter:

letting weight be function
    ( a --> 15
    , b --> 25
    , c --> 45
    , d --> 50
    , e --> 60

given gain : function (total) items --> int

Just the same as the weight parameter, this parameter is used to denote a mapping from each item to a value. An example value for this parameter as it would be defined in the parameter file is:

letting gain be function
    ( a --> 10
    , b --> 20
    , c --> 40
    , d --> 40
    , e --> 50

The final given:

given capacity : int

The final parameter – a weight limit. Example value:

letting capacity be 80

find picked : set of items

The find keyword denotes decision variables, these are the variables for which the solver will search for a valid assignment. As is common in Essence problems, our entire problem is modelled using one decision variable named picked. Its type is set of items; a set of any size whose elements are taken from the items domain. Note, the maximum cardinality of the set is implicitly the size of the items domain.

maximising sum i in picked . gain(i)

The maximising keyword denotes the objective for the solver; a value for the solver to maximise. minimise is also a valid objective keyword. The expression sum i in picked . is a quantifier. The sum says that the values we produce should be summed together. The i in picked says we want to list out every element of the set picked. The expression given to the sum are described by the expression that follows the full-stop (.). In this case, we are asking for the image of i in the gain function. That is, for each item in the set, we are looking up the integer value that the item maps to in the gain function and summing these integers.

such that (sum i in picked . weight(i)) <= capacity

The such that keyword denotes a constraint. Here the constraint is formulated in a similar manner to the objective. We are quantifying over the set of chosen items picked, looking up the value that the item maps to in the weights function and summing these values to together. We enforce that the result of the sum must be less than or equal to the capacity <= capacity.

Note that you can post multiple constraints either by using commas between each constraint , or by reusing the keyword such that.

Solving the problem

Having run the earlier definition cells, we can now run the model:

find picked : set of items
maximising sum i in picked . gain(i)
such that (sum i in picked . weight(i)) <= capacity
{"picked": ["b", "d"]}


We can print the result using python code:

['b', 'd']

We can also show picked as a graph:

import graphviz

p = graphviz.Digraph('parent')

with p.subgraph(name='clusterx', node_attr={'shape': 'pentagon', 'color': 'blue', 'style':'rounded'}) as c:
    for item in picked: