# How to migrate from MIP to LSP ?¶

Since LocalSolver offers operators `sum`, `product` and arithmetic comparisons, any integer program can be directly written in the LocalSolver modeling language, provided that integer variables are rewritten as a weighted sum of binary variables. However, such a model is often not suited for local search and a set of simple operations should be performed on your model in order to reach the highest performance.

Let us consider the car sequencing problem detailed in our step by step example. Here is the standard integer program for this problem, written in LocalSolver syntax, assuming that all variables have been defined beforehand:

```// A MIP-like MODEL FOR THE CAR SEQUENCING PROBLEM
for[c in 1..nbClasses]
constraint sum[p in 1..nbPositions](cp[c][p]) == card[c];

for[p in 1..nbPositions]
constraint sum[c in 1..nbClasses](cp[c][p]) == 1;

for[o in 1..nbOptions][p in 1..nbPositions]
constraint op[o][p] >= sum[c in 1..nbClasses : options[c][o]](cp[c][p]);

for[o in 1..nbOptions][j in 1..nbPositions-Q[o]+1]
constraint nbVehicles[o][j] == sum[k in 1..Q[o]](op[o][j+k-1]);

for[o in 1..nbOptions][j in 1..nbPositions-Q[o]+1] {
constraint violations[o][j] >= nbVehicles[o][j] -P[o];
constraint violations[o][j] >= 0;
}

constraint obj == sum[o in 1..nbOptions][p in 1..nbPositions-Q[o]+1](violations[o][p]);

minimize obj;
```

## Decision variables and intermediate expressions¶

As explained in our modeling principles, the most crucial modeling decision is the choice of the set of decision variables. Here, the set of `cp[c][p]` is a good set of decision variables since the values of all other variables can be inferred from the values of `cp[c][p]`. Now intermediate variables can be written as such. For instance the constraint `nbVehicles[o][j] == sum[k in 1..Q[o]](op[o][j+k-1])` can be turned into an expression defining the term `nbVehicles[o][j] <- sum[k in 1..Q[o]](op[o][j+k-1])`.

## Using non-linear operators instead of linearizations¶

Now we can observe that some equations in this model are in fact linearizations of arithmetic or logic expressions. For instance the constraint `op[o][p] >= sum[c in 1..nbClasses : options[c][o]](cp[c][p])` merely states that `op[o][p]` is equal to 1 as soon as one of the given terms is equal to 1 what is more naturally expressed as a `or`: `op[o][p] <- or[c in 1..nbClasses : options[c][o]](cp[c][p])`

Finally the pair of constraints on `violations[o][j]` are just the linear fashion of defining the maximum of a certain number of variables, what is directly written in LocalSolver as `violations[o][j] <- max( nbVehicles[o][j] -P[o], 0)`.

Similarly, all linearizations of a maximum, a condition or a conjunction should be translated into their direct LocalSolver formulation with operators `max`, `iif` and `and`. Piecewise linear function can be simply written as well. If your MIP contains a piecewise linear function (possibily with associated binary variables) making `Y` equal to `f(X)` such that on any interval `[c[i-1],c[i]]` we have `Y = a[i] * X + b[i]` with i in (1..3), then you will directly define Y as follows: `Y <- X < c ? a*X+b : (X < c ? a*X+b : a*X+b);`

After these transformations we obtain the following model:

```function model() {
cp[1..nbClasses][1..nbPositions] <- bool();

for [c in 1..nbClasses]
constraint sum[p in 1..nbPositions](cp[c][p]) == nbCars[c];

for [p in 1..nbPositions]
constraint sum[c in 1..nbClasses](cp[c][p]) == 1;

op[o in 1..nbOptions][p in 1..nbPositions] <- or[c in 1..nbClasses : options[c][o]](cp[c][p]);

nbCarsWindows[o in 1..nbOptions][p in 1..nbPositions-ratioDenoms[o]+1] <-
sum[k in 1..ratioDenoms[o]](op[o][p+k-1]);

nbViolationsWindows[o in 1..nbOptions][p in 1..nbPositions-ratioDenoms[o]+1] <-
max(nbCarsWindows[o][p]-ratioNums[o], 0);

obj <- sum[o in 1..nbOptions][p in 1..nbPositions-ratioDenoms[o]+1](nbViolationsWindows[o][p]);
minimize obj;
}
```

## Remove useless constraints¶

If your model contains valid inequalities they can (and should) be removed. Since LocalSolver does not rely on a relaxation of the model, these inequalities would only burden the model.

You must omit symetry breaking constraints as well: since LocalSolver does not rely on tree-based search, breaking symetries would just make it harder to move from a feasible solution to another one. Typically it could prevent LocalSolver from considering a nearby improving solution.