The Mixed Integer Programming formulations for solving the puzzles Sudoku and KenKen have a major trait they share: the basic data structure is a binary decision variable:
\[x_{i,j,k} = \begin{cases}1 & \text{if cell $(i,j)$ has the value $k$}\\ 0 & \text{otherwise}\end{cases}\] 
Such a binary variable allows us to easily formulate socalled ‘alldifferent’ constructs (1). E.g. if each row \(i\) having \(n\) cells must have different values \(1..n\) in each cell, we can write:
\[\boxed{\begin{align}&\sum_j x_{i,j,k} = 1 &&\forall i, k&& \text{value $k$ appears once in row $i$}\\ &\sum_k x_{i,j,k} = 1&&\forall i, j&&\text{cell $(i,j)$ contains one value}\\ &x_{i,j,k} \in \{0,1\} \end{align}}\] 
If we want to recover the value of a cell \((i,j)\) we can introduce a variable \(v_{i,j}\) with:
\[v_{i,j} = \sum_k k \cdot x_{i,j,k}\] 
Detail: this is essentially what is sometimes called a set of accounting rows. We could also do this in postprocessing outside the optimization model.
Below I extend this basic structure to a model that solves the Sudoku problem. Next we tackle the KenKen problem. We do this along the lines of (6) but we use a different, more straightforward logarithmic linearization technique for the multiplications and divisions.
Sudoku
On the left we have a Sudoku puzzle and on the right we see the solution (2).
In Sudoku we need uniqueness along the rows, the columns and inside the 3 x 3 subsquares. The constraints that enforce these uniqueness conditions are:
\[\boxed{\begin{align}&\sum_j x_{i,j,k} = 1 &&\forall i, k&& \text{value $k$ appears once in row $i$}\\ &\sum_j x_{i,j,k} = 1 &&\forall j, k&& \text{value $k$ appears once in column $j$}\\ &\sum_{i,ju_{s,i,j}} x_{i,j,k} = 1 &&\forall s, k&& \text{value $k$ appears once in square $s$}\\ &\sum_k x_{i,j,k} = 1&&\forall i, j&&\text{cell $(i,j)$ contains one value} \end{align}}\] 
Here \(u_{s,i,j}\) contains the elements \(i,j\) for square \(s\). With this we have two issues left: How can we build up \(u_{s,i,j}\) and how can we fix the given values in the Sudoku problem.
Populate areas
We number the squares as follows:
Let’s see if we can populate \(u_{s,i,j}\) without entering data manually and without using loops. We can invent a function \(s=f(i,j)\) that takes a row and column number and returns the number of the square as follows:
\[f(i,j) = 3\left\lceil i/3 \right\rceil + \left\lceil j/3 \right\rceil – 3\] 
Here the funny brackets indicate the ceiling function. E.g. \(f(4,7)= 3\left\lceil 4/3 \right\rceil + \left\lceil 7/3 \right\rceil –3 = 3\cdot 2+33=6\). This means we can populate \(u\) as in:
When we take a step back, we can consider rows and columns as just another particularly shaped block. So we add to our set \(u\) the individual rows and columns:
This means we can reduce the model equations to just:
\[\boxed{\begin{align}&\sum_{i,ju_{a,i,j}} x_{i,j,k} = 1 &&\forall a, k&& \text{value $k$ appears once in area $a$}\\ &\sum_k x_{i,j,k} = 1&&\forall i, j&&\text{cell $(i,j)$ contains one value}\\ &x_{i,j,k} \in \{0,1\} \end{align}}\] 
Fixing initial values
If we use the variables \(v_{i,j}\) in the model, we can use them to fix the initial values.
If we don’t have the \(v\) variables in the model we can fix the underlying \(x\) variables:
Complete model
The input is most conveniently organized as a table:
table v0(i,j) c1 c2 c3 c4 c5 c6 c7 c8 c9 r1 5 3 7 r2 6 1 9 5 r3 9 8 6 r4 8 6 3 r5 4 8 3 1 r6 7 2 6 r7 6 2 8 r8 4 1 9 5 r9 8 7 9 ;

The output looks like:
 49 PARAMETER v c1 c2 c3 c4 c5 c6 c7 c8 c9 r1 5 3 4 6 7 8 9 1 2 r2 6 7 2 1 9 5 3 4 8 r3 1 9 8 3 4 2 5 6 7 r4 8 5 9 7 6 1 4 2 3 r5 4 2 6 8 5 3 7 9 1 r6 7 1 3 9 2 4 8 5 6 r7 9 6 1 5 3 7 2 8 4 r8 2 8 7 4 1 9 6 3 5 r9 3 4 5 2 8 6 1 7 9 
KenKen
On the left we have a KenKen puzzle and on the right we see the solution (4).
In the KenKen puzzle we need to have unique values along the rows and the columns. As we have seen in Sudoku example, this is straightforward. We can formulate this as:
\[\boxed{\begin{align}&\sum_j x_{i,j,k} = 1 &&\forall i, k&& \text{value $k$ appears once in row $i$}\\ &\sum_i x_{i,j,k} = 1 &&\forall j, k&& \text{value $k$ appears once in column $j$}\\ &\sum_k x_{i,j,k} = 1&&\forall i, j&&\text{cell $(i,j)$ contains one value}\\
&x_{i,j,k} \in \{0,1\} \end{align}}\] 
As in the Sudoku example, we can combine the first two constraints into a single one:
\[\sum_{i,ju_{s,i,j}} x_{i,j,k} = 1 \>\>\forall s,k\] 
In addition to these constraints we have a slew of restrictions related to collections of cells  these are called the cages. Each cage has a rule and a given answer. Let’s discuss them in detail:
 n this is a single cell with a given value. This case is not present in the data set in our example above. The rule is easy: just put the number in the cell \((i,j)\): \(v_{i,j} = n\),
For this example we would fix this cell to the value 1.  n+ indicates we add up the values in the cage and the result should be n. This is a simple linear constraint: \[\sum_{i,jC{i,j}} v_{i,j} = n\]
 n is a cage with just two cells where the absolute value difference between the values of the two cells is n. I.e. \[v_{i_1,j_1} v_{i_2,j_2}=n\] In (6) it is proposed to model this in a linear fashion with the help of an additional binary variable as: \[v_{i_1,j_1} v_{i_2,j_2}=n(2 \delta1)\] where \(\delta \in \{0,1\}\).
 n* indicates we multiply all values in the cage: \[\prod_{i,jC{i,j}} v_{i,j} = n\] In (6) a number of possible linearizations are mentioned, some of them rather complicated with such things as prime number factorizations. Let me suggest a much simpler approach using logarithms. We have: \[\begin{align}&\prod_{i,jC{i,j}} v_{i,j} = n\\ \Longrightarrow\>\> & \sum_{i,jC_{i,j}} \log(v_{i,j}) = \log(n)\\ \Longrightarrow\>\> & \sum_{i,jC_{i,j}} \sum_k \log(k) x_{i,j,k} = \log(n)\end{align}\] The last constraint is linear in the decision variables \(x_{i,j,k}\)!. We have used here that \(\log(v_{i,j})=\sum_k \log(k) x_{i,j,k}\). This simpler linearization is not mentioned in (6).
Note that we can always evaluate \(\log(k)\) and \(\log(n)\) as \(k\ge 1\) and \(n \ge 1\).  n÷ this operation has two cells, and indicates division and can be described more formally: \[\frac{v_{i_1,j_1}}{v_{i_2,j_2}}=n \>\text{or}\>\frac{v_{i_2,j_2}}{v_{i_1,j_1}}=n\] In (6) the following linearization is proposed:\[\begin{align} &v_{i_1,j_1}n\cdot v_{i_2,j_2} \ge 0M\cdot \delta\\&v_{i_1,j_1}n\cdot v_{i_2,j_2} \le 0+M\cdot \delta\\&v_{i_2,j_2}n\cdot v_{i_1,j_1} \ge 0M\cdot (1\delta)\\&v_{i_2,j_2}n\cdot v_{i_1,j_1} \le 0+M\cdot (1\delta)\\&\delta\in\{0,1\}\end{align}\] Again I would suggest a simpler linearization: \[\begin{align}&\frac{v_{i_1,j_1}}{v_{i_2,j_2}}=n \>\text{or}\>\frac{v_{i_2,j_2}}{v_{i_1,j_1}}=n\\ \Longrightarrow\>\>&\log(v_{i_1,j_1})\log(v_{i_2,j_2})=\log(n) \> \text{or} \>\log(v_{i_2,j_2})\log(v_{i_1,j_1})=\log(n)\\ \Longrightarrow\>\>&\log(v_{i_1,j_1})\log(v_{i_2,j_2})=\log(n)\\ \Longrightarrow\>\>&\log(v_{i_1,j_1})\log(v_{i_2,j_2})=\log(n)(2\delta1)\\ \Longrightarrow\>\>&\sum_k \log(k) x_{i_1,j_1,k}\sum_k \log(k) x_{i_2,j_2,k}=\log(n)(2\delta1)\end{align}\]
I believe we can assume \(n\ge 1\) even for subtraction, hence we can safely apply the logarithm.
Complete Model
We have now all the pieces to assemble the complete model.
The sets are almost identical to the Sudoku model. We have introduced new sets \(op\), \(p\) and \(c\). The unique values will be handled through a set \(u\) as before, only now this set is simpler: only rows and columns (no squares).
We have a somewhat complicated parameter that holds the problem data:
The data can be entered as follows:
parameter operation(c,op,i,j) / cage1 .plus.(r1,r2).c1 11 cage2 .div .r1.(c2,c3) 2 cage3 .min .r2.(c2,c3) 3 cage4 .mul .(r1,r2).c4 20 cage5 .mul .(r1.c5, (r1,r2,r3).c6) 6 cage6 .div .(r2,r3).c5 3 cage7 .mul .(r3,r4).(c1,c2) 240 cage8 .mul .r3.(c3,c4) 6 cage9 .mul .(r4,r5).c3 6 cage10.plus.((r4,r5).c4, r5.c5) 7 cage11.mul .r4.(c5,c6) 30 cage12.mul .r5.(c1,c2) 6 cage13.plus.(r5,r6).c6 9 cage14.plus.r6.(c1*c3) 8 cage15.div .r6.(c4,c5) 2 /; 
We need to extract data from this data structure to make things easier:
The sets \(cells\), \(cellp\) and the parameter \(n\) look like:
 53 SET cells cells in cage c1 c2 c3 c4 c5 c6 cage1 .r1 YES cage1 .r2 YES cage2 .r1 YES YES cage3 .r2 YES YES cage4 .r1 YES cage4 .r2 YES cage5 .r1 YES YES cage5 .r2 YES cage5 .r3 YES cage6 .r2 YES cage6 .r3 YES cage7 .r3 YES YES cage7 .r4 YES YES cage8 .r3 YES YES cage9 .r4 YES cage9 .r5 YES cage10.r4 YES cage10.r5 YES YES cage11.r4 YES YES cage12.r5 YES YES cage13.r5 YES cage13.r6 YES cage14.r6 YES YES YES cage15.r6 YES YES  53 SET cellp ordered cell numbers cage1 .p1.r1.c1 cage1 .p2.r2.c1 cage2 .p1.r1.c2 cage2 .p2.r1.c3 cage3 .p1.r2.c2 cage3 .p2.r2.c3 cage4 .p1.r1.c4 cage4 .p2.r2.c4 cage5 .p1.r1.c5 cage5 .p2.r1.c6 cage5 .p3.r2.c6 cage5 .p4.r3.c6 cage6 .p1.r2.c5 cage6 .p2.r3.c5 cage7 .p1.r3.c1 cage7 .p2.r3.c2 cage7 .p3.r4.c1 cage7 .p4.r4.c2 cage8 .p1.r3.c3 cage8 .p2.r3.c4 cage9 .p1.r4.c3 cage9 .p2.r5.c3 cage10.p1.r4.c4 cage10.p2.r5.c4 cage10.p3.r5.c5 cage11.p1.r4.c5 cage11.p2.r4.c6 cage12.p1.r5.c1 cage12.p2.r5.c2 cage13.p1.r5.c6 cage13.p2.r6.c6 cage14.p1.r6.c1 cage14.p2.r6.c2 cage14.p3.r6.c3 cage15.p1.r6.c4 cage15.p2.r6.c5  53 PARAMETER n value for each cage cage1 11.000, cage2 2.000, cage3 3.000, cage4 20.000, cage5 6.000, cage6 3.000 cage7 240.000, cage8 6.000, cage9 6.000, cage10 7.000, cage11 30.000, cage12 6.000 cage13 9.000, cage14 8.000, cage15 2.000 
The variables in the model are as follows:
The loop to fix some cells is not used in this data set as we don’t have fixed values in the data. The rest of the model looks like:
The summations in the subtraction and divisions are a little bit misleading. These just pick the value of the first (p1) and second (p2) cell in the cage c. In the division equation we pick up the logarithm of the value. Here are the actually generated MIP rows:
 subtraction =E= cages with subtraction subtraction(cage3).. v(r2,c2)  v(r2,c3) + 6*delta(cage3) =E= 3 ; (LHS = 0, INFES = 3 ****)  division =E= cages with division division(cage2).. logv(r1,c2)  logv(r1,c3) + 1.38629436111989*delta(cage2) =E= 0.693147180559945 ; (LHS = 0, INFES = 0.693147180559945 ****) division(cage6).. logv(r2,c5)  logv(r3,c5) + 2.19722457733622*delta(cage6) =E= 1.09861228866811 ; (LHS = 0, INFES = 1.09861228866811 ****) division(cage15).. logv(r6,c4)  logv(r6,c5) + 1.38629436111989*delta(cage15) =E= 0.693147180559945 ; (LHS = 0, INFES = 0.693147180559945 ****) 
In equations \(calclogv\) we could restrict the calculation of \(logv\) only for those cells \((i,j)\) where we have a multiplication or division. Similarly, we can calculate \(v_{i,j}\) only for cells where we do addition or subtraction. Now we calculate too many \(logv\)’s and \(v\)'s. For this small case this is not an issue. Furthermore a good LP/MIP presolver will probably detect a variable that is not used anywhere else, and will remove the variable and the accompanying equation.
The final result looks like:
 101 VARIABLE v.L value c1 c2 c3 c4 c5 c6 r1 5.000 6.000 3.000 4.000 1.000 2.000 r2 6.000 1.000 4.000 5.000 2.000 3.000 r3 4.000 5.000 2.000 3.000 6.000 1.000 r4 3.000 4.000 1.000 2.000 5.000 6.000 r5 2.000 3.000 6.000 1.000 4.000 5.000 r6 1.000 2.000 5.000 6.000 3.000 4.000

Conclusion
To solve the KenKen puzzle as a Mixed Integer Program, we can start with a data structure similar as used in the Sodoku formulation: \(x_{i,j,k}\in\{0,1\}\). Using different linearizations from the ones shown in (6) we can solve then KenKen problem easily with a MIP solver.
References
 Alldifferent and Mixed Integer Programming, http://yetanothermathprogrammingconsultant.blogspot.com/2016/05/alldifferentandmixedinteger.html
 Sudoku, https://en.wikipedia.org/wiki/Sudoku
 Sudoku Puzzle, http://www.nytimes.com/crosswords/game/sudoku/hard
 KenKen, https://en.wikipedia.org/wiki/KenKen
 KenKen Puzzle, http://www.nytimes.com/ref/crosswords/kenken.html
 Vardges Melkonian, “An Integer Programming Model for the KenKen Problem”, American Journal of Operations Research, 2016, 6, 213225. [link] Contains a complete AMPL model.
 Andrew C. Bartlett, Timothy P. Chartier, Amy N. Langville, Timothy D. Rankin, “An Integer Programming Model for the Sudoku Problem”, The Journal of Online Mathematics and Its Applications, Vol.8, 2008, [link]