AMPL nonquadratic nonlinear constraints with cplex - optimization

I'm working on an optimization project and I faced a small problem. For my project, I'm using AMPL and CPLEX as a solver. In my code, I have some elements indicated by e1, e2, ..., en. I also have a set which contains tuples within these elements. I must assign a number between 1 and 'n' to each element such that I maximize the distance between every 2 elements in 1 tuple in the moveTuples set (I need to order them but try to keep a distance between elements in the same tuple).
Every element MUST have ONLY 1 assigned number and each number should be given to ONLY 1 element. For this purpose, I wrote the following code:
set Elements;
set moveTuples dimen 2;
set Numbers;
var assign {Elements,Numbers} binary;
var maximizer{moveTuples} integer >= 0;
maximize obj: sum {(A,B) in moveTuples} maximizer[A,B];
subject to assign1NumberPerElement {i in Element}: sum {c in Numbers} assign[i,c] = 1;
subject to assign1ElementPerNumber {c in Numbers}: sum {i in Element} assign[i,c] = 1;
subject to moveApart {(A,B) in moveTuples}: abs(sum{i in Numbers}(assign[A,i]*i) - (sum{j in Numbers}x[B,j]*j)) - maximizer[A,B] = 0 ;
data;
set Elements:= e1 e2 e3;
set Numbers:= 1 2 3;
set moveTuples: e1 e2 e3:=
(e1, e2);
solve;
display assign;
Now the problem is clear and for the previous example, the output must be either:
e1 -> 1
e2 -> 3
e3 -> 2
or
e1 -> 3
e2 -> 1
e3 -> 2
since it is required to only move e1 from e2 using the tuple (e1,e2). When running the previous code, I get the error: ... contains a non-quadratic non-linear constraint (definitely the "moveApart" constraint). Can you please guide me on how to solve this problem? Thanks in advance.

The constraint moveApart is nonlinear (and non-quadratic) because it contains a call to abs. However, since your problem is purely integer, you can solve it with CPLEX CP Optimizer (ilogcp).

Related

Indexing variables in sets in Xpress Mosel

I'm trying to solve a linear relaxation of a problem I've already solved with a Python library in order to see if it behaves in the same way in Xpress Mosel.
One of the index sets I'm using is not the typical c=1..n but a set of sets, meaning I've taken the 1..n set and have created all the combinations of subsets possible (for example the set 1..3 creates the set of sets {{1},{2},{3},{1,2},{2,3},{1,2,3}}).
In one of my constraints, one of the indexes must run inside each one of those subsets.
The respective code in Python is as follows (using the Gurobi library):
cluster=[1,2,3,4,5,6]
cluster1=[]
for L in range(1,len(cluster)+1):
for subset in itertools.combinations(cluster, L):
clusters1.append(list(subset))
ConstraintA=LinExpr()
ConstraintB=LinExpr()
for i in range(len(nodes)):
for j in range(len(nodes)):
if i<j and A[i][j]==1:
for l in range(len(clusters1)):
ConstraintA+=z[i,j]
for h in clusters1[l]:
restricao2B+=(x[i][h]-x[j][h])
model.addConstr(ConstraintA,GRB.GREATER_EQUAL,ConstraintB)
ConstraintA=LinExpr()
ConstraintB=LinExpr()
(In case the code above is confusing, which I suspect it to be)The constraint I'm trying to write is:
z(i,j)>= sum_{h in C1}(x(i,h)-x(j,h)) forall C1 in C
in which the C1 is each of those subsets.
Is there a way to do this in Mosel?
You could use some Mosel code along these lines (however, independently of the language that you are using, please be aware that the calculated 'set of all subsets' very quickly grows in size with an increasing number of elements in the original set C, so this constraint formulation will not scale up well):
declarations
C: set of integer
CS: set of set of integer
z,x: array(I:range,J:range) of mpvar
end-declarations
C:=1..6
CS:=union(i in C) {{i}}
forall(j in 1..C.size-1)
forall(s in CS | s.size=j, i in C | i > max(k in s) k ) CS+={s+{i}}
forall(s in CS,i in I, j in J) z(i,j) >= sum(h in s) (x(i,h)-x(j,h))
Giving this some more thought, the following version working with lists in place of sets is more efficient (that is, faster):
uses "mmsystem"
declarations
C: set of integer
L: list of integer
CS: list of list of integer
z,x: array(I:range,J:range) of mpvar
end-declarations
C:=1..6
L:=list(C)
qsort(SYS_UP, L) ! Making sure L is ordered
CS:=union(i in L) [[i]]
forall(j in 1..L.size-1)
forall(s in CS | s.size=j, i in L | i > s.last ) CS+=[s+[i]]
forall(s in CS,i in I, j in J) z(i,j) >= sum(h in s) (x(i,h)-x(j,h))

Define the function for distance matrix in ampl. Keep getting "i is not defined"

I'm trying to set up a ampl model which clusters given points in a 2-dimensional space according to the model of Saglam et al(2005). For testing purposes I want to generate randomly some datapoints and then calculate the euclidian distance matrix for them (since I need this one). I'm aware that I could only make the distance matrix without the data points but in a later step the data points will be given and then I need to calculate the distances between each the points.
Below you'll find the code I've written so far. While loading the model I keep getting the error message "i is not defined". Since i is a subscript that should run over x1 and x1 is a parameter which is defined over the set D and have one subscript, I cannot figure out why this code should be invalid. As far as I understand, I don't have to define variables if I use them only as subscripts?
reset;
# parameters to define clustered
param m; # numbers of data points
param n; # numbers of clusters
# sets
set D := 1..m; #points to be clustered
set L := 1..n; #clusters
# randomly generate datapoints
param x1 {D} = Uniform(1,m);
param x2 {D} = Uniform(1,m);
param d {D,D} = sqrt((x1[i]-x1[j])^2 + (x2[i]-x2[j])^2);
# variables
var x {D, L} binary;
var D_l {L} >=0;
var D_max >= 0;
#minimization funcion
minimize max_clus_dis: D_max;
# constraints
subject to C1 {i in D, j in D, l in L}: D_l[l] >= d[i,j] * (x[i,l] + x[j,l] - 1);
subject to C2 {i in D}: sum{l in L} x[i,l] = 1;
subject to C3 {l in L}: D_max >= D_l[l];
So far I tried to change the line form param x1 to
param x1 {i in D, j in D} = ...
as well as
param d {x1, x2} = ...
Alas, nothing of this helped. So, any help someone can offer is deeply appreciated. I searched the web but I found nothing useful for my task.
I found eventually what was missing. The line in which I calculated the parameter d should be
param d {i in D, j in D} = sqrt((x1[i]-x1[j])^2 + (x2[i]-x2[j])^2);
Retrospectively it's clear that the subscripts i and j should have been mentioned on the line, I don't know how I could miss that.

Let Σ={a,b,c}. How many languages over Σ are there such that each string in the language has length 2 or less?

First of all I see the number of strings as the following:
1 (epsilon 0 length string) + 3 (pick one letter) + 9 (3 options for first letter, 3 options for second)
For a total of 13 strings. Now as far as I know a language can pick any combination of this for example l1 = {ab,a,ac} l2 = {c}
I'm not sure how to calculate the total number of languages there could be here. Any Help?
So you have a set with 13 elements. A particular language could be any subset of this set. How many subsets does this set have?
This is called the power set of that set, and it has 213 elements.
Cardinality of character set, say d = 3.
Total words possible of length (<= k), say w = (d^(k+1) - 1)/(d-1) = 13.
Total languages possible = Power Set {Each word can be included or not} = 2^w = 8192.

SAS Proc Optmodel Syntax and Strategy

I have a data set that looks like this (SAS 9.4):
data opt_test;
input ID GRP $ x1 MIN MAX y z;
cards;
2 F 10 9 11 1.5 100
3 F 10 9 11 1.2 50
4 F 11 9 11 .9 20
8 G 5 4 6 1.2 300
9 G 6 4 6 .9 200
;
run;
I want to create a new variable x2 that maximizes a function based on x1, x2, y, and z.
I am having two main problems:
The syntax on my proc optmodel has some errors that I have not been able to fix "Subscript 1 may not be a set" and constraint has incomplete declaration". UPDATE: I figured this part out.
I need for the value of x2 to be the same for all members of the same GRP. So, id 2,3,4 would have same x2. ID 8 and 9 would have same x2.
Below is my attempt. This will ultimately be able to run with sevarl different GRP of varying numbers of ID.
Thanks in advance for any assistance.
proc optmodel;
set<num> ID;
var x2{ID} >= 0;
string GRP{ID};
number x1{ID};
number MIN{ID};
number MAX{ID};
number y{ID};
number z{ID};
max sales=sum{i in ID}(x2[i])*(1-(x2[i]-x1[i])*y[i]/x1[i])*z[i];
con floor_METRIC1{i in ID}: x2[i]>=MIN[i];
con ceiling_METRIC1{i in ID}: x2[i]<=MAX[i];
read data opt_test into
ID=[ID]
GRP
x1
MIN
MAX
y
z
;
solve;
print x2;
quit;
If you want the value of x2 to be the same for all ids in the same group, then you only need one variable x2 per group. To keep track of which ids are in which group you could use an array of sets indexed by group:
set<num> ID;
string GRP{ID};
set GRPS = setof{i in ID} GRP[i];
set IDperGRP{gi in GRPS} = {i in ID: GRP[i] = gi};
When you use = (as opposed to init), you provide OPTMODEL with a function you don't need to update later. If you change any of the GRP or ID data, optmodel will recompute GRPS and IDperGRP as needed.
Now you can use the GRPS set and the IDperGRP array of sets to rewrite your objective to more closely match your business rule:
max sales = sum{gi in GRPS} sum{i in IDperGRP[gi]}
(x2[gi]) * (1-(x2[gi]-x1[i])*y[i]/x1[i]) * z[i];
Writing the expression this way makes it clearer (to me at least) that it can be simplified further as long as x1, y, and z are constants.
Adding the new sets also makes it clearer (to me at least) that the bounds from floor_METRIC1 and ceiling_METRIC1 can be combined to tighten the domain of x2. Since MIN and MAX are constants, you can move the constraints into direct variable bounds by adding >= and <= clauses to the declaration of x2. Since x2 will now depend on MIN and MAX, you will have to declare those before x2. IMHO that makes your intent clearer:
number MIN{ID};
number MAX{ID};
var x2{gi in GRPS} >= max{i in IDperGRP[gi]} MIN[i]
<= min{i in IDperGRP[gi]} MAX[i]
;

AMPL: Subscript out of bound

Hello fellow optimizers!
I'm having some issues with the following constraint:
#The supply at node i equals what was present at the last time period + any new supply and subtracted by what has been extracted from the node.
subject to Constraint1 {i in I, t in T, c in C}:
l[i,t-1,c] + splus[i,t] - sum{j in J, v in V, a in A} x[i,j,v,t,c,a]= l[i,t,c];
which naturally causes this constraint to provide errors the first time it loops, as the t-1 is not defined (for me, l[i,0,c] is not defined. Where
var l{I,T,C} >= 0; # Supply at supply node I in time period T for company C.
param splus{I,T}; # Additional supply at i.
var x{N,N,V,T,C,A} integer >= 0; #Flow from some origin within N to a destination within N using vehicle V, in time T, for company C and product A.
and set T; (in the .mod) is a set defined as:
set T := 1 2 3 4 5 6 7; in the .dat file
I've tried to do:
subject to Constraint1 {i in I, t in T: t >= 2, c in C}:
all else same
which got me a syntax error. I've also tried to include "let l[1,0,1] := 0" for all possible combinations, which got me the error
error processing var l[...]:
no data for set I
I've also tried
subject to Constraint1 {i in I, t in T, p in TT: p>t, c in C}:
l[i,t,c] + splus[i,p] - sum{j in J, v in V, a in A} x[i,j,v,p,c,a]= l[i,p,c];
where
set TT := 2 3 4 5 6;
in the .dat file (and merely set TT; in the .mod) which also gave errors. Does someone have any idea of how to do this?
One way to fix this is to specify the condition t >= 2 at the end of the indexing expression:
subject to Constraint1 {i in I, t in T, c in C: t >= 2}:
...
See also Section A.3 Indexing expressions and subscripts for more details on syntax of indexing expressions.