Sets in AMPL

Set expressions may also involve one or more set operators:

1. A union B gives the set of elements in either A or B;
2. A inter B gives the set of elements in both A and B;
3. A diff B gives the set of elements in A that are not in B;
4. A symdiff B gives the set of elements in either A or B but not both;
5. A cross B gives the two-dimensional set of all pairs a A, b B. This can also be defined by {a in A, b in B}.

You will see examples of set expressions throughout the rest of this page.

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Defining a Set

Sets are usually defined in a data file:

set NODES := Youngstown Pittsburgh Cincinnati 'Kansas City' Chicago              Albany Houston Tempe Gary ; 

although they may be defined during declaration using either an explicit set literal or using a set expression:

set KIND := {'HOST', 'DEVICE', 'SWITCH', 'HUB', 'LINK', 'SUPERLINK'};  set COMPONENT := {C in CLASSES : (kind[C] = 'HOST'  ) or              (kind[C] = 'DEVICE') or              (kind[C] = 'HUB'   ) or              (kind[C] = 'SWITCH')};  set FABRIC := NODE union LINK; 

and sets may also be defined dynamically:

set SEARCH within VERTICES; let SEARCH := {v in VERTICES: (v, w) in EDGES}; 

Defining 2-Dimensional Sets

There are three different ways to define 2-dimensional sets. The "best" way to use depends on the set.

1. Using a List You simply list the elements in the set. This is good for sparse sets.

   model; set ARCS within NODES cross NODES; data; set ARCS := (Youngstown, Albany), (Youngstown, Cincinnati), ... ; 

2. Using a Table You give a table using the first index set for the rows and the second index set for the columns, then you place a + where an element exists and a - where there is no element. This is good for dense sets.

   set ARCS: Cincinnati 'Kansas City' Chicago Albany Houston Tempe Gary := Youngstown + + + + - - - Pittsburgh + + + - - - + Cincinnati - - - + + - - 'Kansas City' - - - - + + - Chicago - - - - - + + ; 

3. Using an Array You define a list of column indices for each row index. This is a good for sets with a few elements for each row.

   set ARCS := (Youngstown, *) Cincinnati ‘Kansas City’ Chicago Albany (Pittsburgh, *) Cincinnati ‘Kansas City’ Chicago Gary (Cincinnati, *) Albany Houston ... 

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Ordered Sets

You can create sets where the elements are ordered using the ordered keyword during definition.

set MONTHS ordered; 

AMPL will put the elements in this set in the order they appear in the data file (or let statement). AMPL also understands the following operations for ordered sets:

ord(e, ORD_SET)   # The position of e in ORD_SET first(ORD_SET)    # The first element in ORD_SET last(ORD_SET)     # The last element in ORD_SET prev(e, ORD_SET)  # The element before e in ORD_SET next(e, ORD_SET)  # The element after e in ORD_SET member(i, ORDSET) # The element at position i in ORD_SET 

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Set Example

Consider the following AMPL statement from the The American Steel Planning Problem. We use ord in the creation of TIME_ARCS:

# The set of time-staged arcs set TIME_ARCS within TIME_NODES cross TIME_NODES :=    { (m, t) in TIME_NODES, (n, u) in TIME_NODES :      ( ( (m, n) in ARCS) and (t = u) ) or # The transportation arcs      ( (m = n) and (ord(t, MONTHS) + 1 = ord(u, MONTHS)) )}; # The storage arcs 

There are many concepts within this one statement, let's look at them one at a time.

Set Operations

There are many operations we can perform on sets (see Set Expressions). We have seen that cross creates all pairs of two sets, so TIME_NODES cross TIME_NODES creates a set of all pairs of TIME_NODES.

Some set operations may be looped over indexing sets. For example, to generate all the transportation arcs in the time-staged network you could use the following statement:

set TRANSPORT_ARCS := union {t in MONTHS} (union {(m, n) ARCS} {(m, t, n, t)}); 

set TRANSPORT_ARCS := union {t in MONTHS, (m, n) in ARCS} {(m, t, n, t)}; 

Set Membership and Subsets

We have seen how to loop over a set using the in keyword. This keyword also provides a logical check if an element is in a set, e.g., (m, n) in ARCS is true if the pair (m, n) is in the set ARCS and false otherwise. We may restrict a set to be a subset of an existing set by using the keyword within, e. g.,

set ARCS within NODES cross NODES; 

Ordered Set Operators

The final condition on TIME_ARCS

    ( (m = n) and (ord(t, MONTHS) + 1 = ord(u, MONTHS)) )}; 

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Restricted Sets

When using display or printf statements we saw that we could restrict the members of a set being printed, e.g.,

display {(m, n) in ARCS : Supply[m] > 0}; # Display all arcs from supply nodes 

We can do this when creating sets, e. g., TIME_NODES, or when using a set as an index for variables, parameters or constraints. For example, rather than setting the upper bound of UnderProduction to be 0 for all non-supply nodes (since non-supply nodes don't produce anything) we could only create this variable for the supply nodes (in fact this may be preferable since there will be less variables).

var UnderProduction {(n, t) in TIME_NODES : Supply[n, t] > 0} >= 0, integer; 

We could make sure this variable is only added to the constraints for the supply nodes (e.g., ConserveFlow constraints) by using a conditional expression.

subject to ConserveFlow {(n, t) in TIME_NODES}:    sum {(m, s) in TIME_NODES: (m, s, n, t) in TIME_ARCS}      Shipment[m, s, n, t] + Supply[n, t] -      if Supply[n, t] > 0 then UnderProduction[n, t] = ...  

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Multi-dimensional Sets

We have already seen different ways of declaring 2-dimensional sets. We have now encountered higher dimensional sets, e.g., TIME_ARCS has 4 dimensions. We generated TIME_ARCS automatically, but we could have specified it using a data file.

set TIME_ARCS within TIME_NODES cross TIME_NODES; 

List

set TIME_ARCS := (Youngstown, April, Albany, April)                   (Youngstown, April, Youngstown, May) ... ; 

Table

set TIME_ARCS : = (*, May, *, May)  Cincinnati 'Kansas City' Albany ... := Youngstown         +          +            +      ... Pittsburgh         +          +            -      ... ... ; 

Array

set TIME_ARCS := (*, May, *, May) (Youngstown, Cincinnati) ... ... ; 

or

set TIME_ARCS := (Youngstown, May, *, May) Cincinnati 'Kansas City' ... ... ; 

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Efficient Generation of Sets

When creating models, generating sets by looping over all possibilities and removing those that don't fit some conditions (e.g., like the TIME_ARCS statement) can take a long time if there are many possibilities. This is time that could be spent solving the model! If possible you should try to create sets by building them up from smaller building block, rather than by creating an enormous set and pruning it. For example, instead of using this statement

# The set of time-staged arcs set TIME_ARCS within TIME_NODES cross TIME_NODES :=    { (m, t) in TIME_NODES, (n, u) in TIME_NODES :      ( ( (m, n) in ARCS) and (t = u) ) or # The transportation arcs      ( (m = n) and (ord(t, MONTHS) + 1 = ord(u, MONTHS)) )}; # The storage arcs 

to create TIME_ARCS you could use these statements

set TRANSPORT_ARCS := union {t in MONTHS, (m, n) in ARCS} {(m, t, n, t)}; set STORAGE_ARCS := union {t in MONTHS, (m, n) in ARCS : t <> last(MONTHS)}    {(m, t, n, next(t, MONTHS)};  set TIME_ARCS within TIME_NODES cross TIME_NODES :=   TRANSPORT_ARCS union STORAGE_ARCS; 

Rather than looping over all possibilities and only keeping those that are appropriate, the new statement only loops over smaller sets that can be used to build up the TIME_NODES set efficiently.

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-- MichaelOSullivan - 27 Feb 2008