Modeling equality in an ILP Lets say we have integer variables $a \in\mathbb{Z}^n$  and  $M \in\{0,1\}^{n\times L}$. I am promised $a_i \leq L$, for some fixed constant $L$.  I want to model the constraint $$M_{i,j} \iff (a_i=j)$$
in an integer linear program.  What is the most efficient way to do this in an ILP?  In particular, can I do this without Big-M constraints and without breaking down each integer into its binary representation?

Currently I can see two ways to model this:

Option 1

We can apply the method described in the answer here to the constraint $M_{i,j} \iff (a_i-j=0)$.  This requires $1$ extra binary variable as well as  $4$ potentially large "Big M" constraints per $M_{i,j}$, thus resulting in $nL$ new binary variables and $4nL$ new big M constraints.

Option 2

We can break down $a_i$ into its bits using $\log L$ extra binary variables. We can also compute the corresponding binary bit breakdowns for each $0\leq j \leq L$. Let $bit^{(a)}_{i,k}$ be the $k$th bit of $a_i$ and let $bit_{j,k}^*$ be the $k$th bit of the integer $j$, so that 
$$a_i = \sum_{k=0}^{\log L}2^kbit^{(a)}_{i,k}.$$

We now have the following logic:
$$M_{i,j} \iff (\forall k . 0\leq k\leq \log L \implies bit^{(a)}_{i,k} \odot bit_{j,k}^*)$$

Therefore

$$M_{i,j} = \bigwedge_{k=0}^{\log L} bit^{(a)}_{i,k} \odot bit_{j,k}^*.$$

We can use this post to NXOR the pairs of variables, storing their results as another variable $z=\{0,1\}^{\log L}$. We can AND these together using the constraints $$M_{i,j} \ge \sum z_i$$ $$M_{i,j} \leq  z_i \;\;\; \forall i$$

This creates $\log L$ new variables and 1 constraint for representing the binary breakdown of $a_i$. We then have $\log L$ XNOR variables, each which can be represented with 4 constraints. We can finally represent $M_{i,j}$ with  $\log L + 1$ more constraints. Therefore, we have a total of $5\log L$ constraints and $2\log L$ new variables per $M_{i,j}$, giving us a total of $5nL\log L$ new binary constraints and $2nL\log L$ new binary variables. 

Is there some third option that is better than either of these two options?
I have several options for you.  Here is the simplest:

1.1 Direct Option

Use an inequality to enforce that one out of $M_{i,1},\dots,M_{i,L}$ are true:

$$M_{i,1} + \dots + M_{i,L} = 1.$$

Use a second inequality to link that to $a_i$:

$$a_i = M_{i,1} + 2 M_{i,2} + 3 M_{i,3} + \dots + L M_{i,L}.$$

2 Indirect Options

Alternatively, we could try to follow more closely to the approach you outlined, but using ideas from one-out-of-n constraints for SAT solvers.  In particular, the entries $M_{i,1},\dots,M_{i,L}$ are a one-hot encoding of a one-out-of-L constraint, and you want them to be consistent with an encoding directly as an integer.  There are standard techniques for enforcing one-out-of-n constraints for SAT, and you could using them with ILP as well.

2.1 Improved Binary Encoding

We can use the idea in https://cs.stackexchange.com/a/51512/755 to improve your option 2.  Let $a_{i,k}$ denote the $k$th bit of $a$.  We add the constraint

$$M_{i,j} \implies a_{i,k} \text{ if } bit^*_{j,k}=1,$$

otherwise add the constraint

$$M_{i,j} \implies \neg a_{i,k} \text{ if } bit^*_{j,k}=0.$$

Next, we add the constraint

$$M_{i,1} + \dots + M_{i,L} \ge 1.$$

This requires only $n+nL\log L$ constraints and $n\log L$ new variables, instead of $5nL \log L$ constraints and $2nL \log L$ new variables.

Commander Encoding

You could use a a recursive commander encoding to enforce that exactly one out of $M_{i,1},\dots,M_{i,L}$ are true.  Note that the constraint for "at most one variable in a group can be true" requires $O(m^2)$ clauses in SAT but can be represented in a single inequality in ILP, so the number of constraints needed will be smaller than for SAT.  Then, you could use any of the above techniques to link the $M$'s to the $a'$.

That said, I don't expect these indirect options to be worth implementation; my expectation is that the direct option will be more natural, and it is what I would try first.