Slides for my talk at Scala By The Bay 2015 http://scalabythebay2015.sched.org/event/8cbbc52523ac9e5a905361e00d357099 GNU Mathprog is an algebraic modeling language for describing mathematical programming models. The language is a subset of AMPL, the most popular language used for describing such models. One advantage of this languages is the similarity of its syntax with the mathematical notation used to describe optimization problems. In this talk we describe the use of Scala in the construction of an embedded DSL to work with Mathprog models. At construction time, the DSL works as an alternative way to build the AST representing the model, taking advantage of the Scala compiler to check the validity of most expressions. The AST can be created also by parsing Mathprog model files. Once an AST is obtained, the DSL allows to manipulate and transform the AST to extract data or create new models. We show how the use of type classes and implicit conversion prioritization gives us a systematic way to define operators, valid combinations of parameters and result types, and conversions between expressions. https://github.com/gerferra/amphip

1. Germán Ferrari
Instituto de Computación, Universidad de la República, Uruguay
An embedded DSL to
manipulate MathProg
Mixed Integer
Programming
models within Scala
An embedded DSL to
manipulate MathProg
Mixed Integer
Programming
models within Scala
Germán Ferrari
Instituto de Computación, Universidad de la República, Uruguay

2. GNU MathProg
MathProg = Mathematical Programming
Has nothing to do with computer programming
It’s a synonym of “mathematical optimization”
A “program” here refers to a plan or schedule

3. GNU MathProg
It’s a modeling language
Optimization problem
Mathematical modeling
GNU MathProg
Computational experiments
Supported by the GNU Linear Programming Kit

4. GNU MathProg
Supports linear models, with either real or
integer decision variables
This is called “Mixed Integer Programming”
(MIP) because it mixes integer and real
variables

5. Generic MIP optimization problem
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];

6. Generic MIP optimization problem
Find x[j] and y[k], that minimize a function f,
satisfying constraints g[i]
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];

7. Generic MIP optimization problem
Find x[j] and y[k], that minimize a function f,
satisfying constraints g[i]
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];
Decision variables
(real or integer)

8. Generic MIP optimization problem
Find x[j] and y[k], that minimize a function f,
satisfying constraints g[i]
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];
Decision variables
(real or integer)
Objective
function

9. Generic MIP optimization problem
Find x[j] and y[k], that minimize a function f,
satisfying constraints g[i]
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];
Decision variables
(real or integer)
Objective
function
Constraints

10. Generic MIP optimization problem
Find x[j] and y[k], that minimize a function f,
satisfying constraints g[i]
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];
Decision variables
(real or integer)
Objective
function
Constraints
Linear

11. Generic MIP optimization problem
Full model
param m; param n; param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J}; param d{K};
param a{I, J}; param e{I, K}; param b{I};
var x{J} integer, >= 0;
var y{K} >= 0;
minimize f: sum{j in J} c[j] * x[j] + sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] + sum{k in K} e[i,k] * y[k] <= b[i];

12. param m; param n; param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J}; param d{K};
param a{I, J}; param e{I, K}; param b{I};
var x{J} integer, >= 0;
var y{K} >= 0;
minimize f: sum{j in J} c[j] * x[j] + sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] + sum{k in K} e[i,k] * y[k] <= b[i];
Generic MIP optimization problem
Declaration of
variables

13. param m; param n; param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J}; param d{K};
param a{I, J}; param e{I, K}; param b{I};
var x{J} integer, >= 0;
var y{K} >= 0;
minimize f: sum{j in J} c[j] * x[j] + sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] + sum{k in K} e[i,k] * y[k] <= b[i];
Generic MIP optimization problem
Parameters

14. Generic MIP optimization problem
param m; param n; param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J}; param d{K};
param a{I, J}; param e{I, K}; param b{I};
var x{J} integer, >= 0;
var y{K} >= 0;
minimize f: sum{j in J} c[j] * x[j] + sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] + sum{k in K} e[i,k] * y[k] <= b[i];
Indexing sets

15. Why MathProg in Scala

16. Why MathProg in Scala
Generate constraints
Create derived models
Develop custom resolution methods at high
level
...

17. MathProg in Scala (deep embedding)
Syntax
Represent MathProg models with Scala data
types
Mimic MathProg syntax with Scala functions
Semantics
Generate MathProg code, others ...

18. MathProg in Scala (deep embedding)
Syntax
Represent MathProg models with Scala data
types
Mimic MathProg syntax with Scala functions
Semantics
Generate MathProg code, others ...
Abstract syntax tree

19. MathProg in Scala (deep embedding)
Syntax
Represent MathProg models with Scala data
types
Mimic MathProg syntax with Scala functions
Semantics
Generate MathProg code, others ...
Smart constructors and
combinators
Abstract syntax tree

20. MathProg EDSL
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0

21. MathProg EDSL
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
set
param
xvar ...

22. MathProg EDSL
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
set
param
xvar ...
ParamStat(
"c",
domain = Some(
IndExpr(
List(
IndEntry(
Nil,
SetRef(J)
)
))))

23. val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
MathProg EDSL
Scala range
notation for
arithmetic
sets
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;

24. val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
MathProg EDSL
Varargs for
simple indexing
expressions

25. val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
MathProg EDSL
Returns a new
variable with the
`integer’ attribute

26. val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
MathProg EDSL
Another variable,
now adding the
lower bound
attribute
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;

27. val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
MathProg EDSL
Everything is
immutable

28. MathProg EDSL
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}

29. MathProg EDSL minimize
st ...
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}

30. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL Multiple
parameters lists

31. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL Multiple
parameters lists

32. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL addition,
multiplication,
summation ...

33. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL References to
individual parameters
and variables by
application

34. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL
Constraint

35. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL
Creates a new
constraint with the
specified name and
indexing

36. Implementing the syntax

37. Implementing the syntax
Many of the syntactic constructs require:
A broad (open?) number of overloadings
Be used with infix notation

38. Many of the syntactic constructs require:
A broad (open?) number of overloadings
Be used with infix notation
Implementing the syntax
type classes and
object-oriented forwarders

39. Many of the syntactic constructs require:
A broad (open?) number of overloadings
Be used with infix notation
Implementing the syntax
type classes and
object-oriented forwarders
implicits prioritization

40. Implementing the syntax: “>=”
param("p", J) >= 0

41. Implementing the syntax: “>=”
param("p", J) >= 0
ParamStat

42. Implementing the syntax: “>=”
param("p", J) >= 0
ParamStat
ParamStat doesn’t have a `>=’
method

43. Implementing the syntax: “>=”
param("p", J) >= 0
implicit conversion to
something that
provides the method
ParamStat doesn’t have a `>=’
method
ParamStat

44. Implementing the syntax: “>=”
param("p", J) >= 0 // ParamStat
param("p", J) >= p2 // ParamStat
xvar("x", I) >= 0 // VarStat
i >= j // LogicExpr
p(j1) >= p(j2) // LogicExpr
x(i) >= 0 // ConstraintStat
10 >= x(i) // ConstraintStat
Many overloadings
… more

45. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}

46. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
Fully generic

47. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
trait GTEOp[A,B,C] {
def gte(lhe: A, rhe: B): C
}
Type class

48. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
trait GTEOp[A,B,C] {
def gte(lhe: A, rhe: B): C
}
Type class
The implementation is
forwarded to the implicit
instance

49. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
The available implicit instances
encode the rules by which the
operators can be used

50. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
The instance of the type class is
required at the method level, so
both A and B can be used in the
implicits search

51. Implementing the syntax: “>=”
instances
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr):
GTEOp[ParamStat, A, ParamStat] = /* ... */
>= for parameters and “numbers”
param(“p”) >= m

52. Implementing the syntax: “>=”
instances
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr):
GTEOp[ParamStat, A, ParamStat] = /* ... */
>= for parameters and “numbers”
param(“p”) >= m

53. Implementing the syntax: “>=”
instances
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr):
GTEOp[ParamStat, A, ParamStat] = /* ... */
>= for parameters and “numbers”
param(“p”) >= m
View bound

54. Implementing the syntax: “>=”
instances
implicit def VarStatGTEOp[A](
implicit conv: A => NumExpr):
GTEOp[VarStat, A, VarStat] = /* ... */
>= for variables and “numbers”
xvar(“x”) >= m
Analogous to the
parameter case

55. Implementing the syntax: “>=”
instances
implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr):
GTEOp[A, B, LogicExpr] = /* ... */
>= for two “numbers”
i >= j

56. Implementing the syntax: “>=”
instances
implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr):
GTEOp[A, B, LogicExpr] = /* ... */
>= for two “numbers”
i >= j

57. Implementing the syntax: “>=”
instances
implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr):
GTEOp[A, B, LogicExpr] = /* ... */
>= for two “numbers”
i >= j

58. Implementing the syntax: “>=”
instances
implicit def LinExprGTEOp[A, B](
implicit convA: A => LinExpr,
convB: B => LinExpr):
GTEOp[A, B, ConstraintStat] = /* ... */
>= for two “linear expressions”
x(i) >= 0 and 10 >= x(i)
Analogous to the
numerical
expression case

59. implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr)
conflicts with:
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr)
implicit def LinExprGTEOp[A, B](
implicit convA: A => LinExpr,
convB: B => LinExpr)
Instances are ambiguous

60. implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr)
conflicts with:
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr)
implicit def LinExprGTEOp[A, B](
implicit convA: A => LinExpr,
convB: B => LinExpr)
Instances are ambiguous
`ParamStat <% NumExpr‘
to allow
`param(“p2”) >= p1’

61. Instances are ambiguous
implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr)
conflicts with:
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr)
implicit def LinExprGTEOp[A, B](
implicit convA: A => LinExpr,
convB: B => LinExpr)
NumExpr <: LinExpr
=>
NumExpr <% LinExpr

62. Implicits prioritization
trait GTEInstances extends GTEInstancesLowPriority1 {
implicit def ParamStatGTEOp[A](implicit conv: A => NumExpr) /* */
implicit def VarStatGTEOp[A](implicit conv: A => NumExpr) /* */
}
trait GTEInstancesLowPriority1 extends GTEInstancesLowPriority2 {
implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr, convB: B => NumExpr) /* */
}
trait GTEInstancesLowPriority2 {
implicit def LinExprGTEOp[A, B](
implicit convA: A => LinExpr, convB: B => LinExpr) /* */
}

63. Implicits prioritization
trait GTEInstances extends GTEInstancesLowPriority1 {
implicit def ParamStatGTEOp[A](implicit conv: A => NumExpr) /* */
implicit def VarStatGTEOp[A](implicit conv: A => NumExpr) /* */
}
trait GTEInstancesLowPriority1 extends GTEInstancesLowPriority2 {
implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr, convB: B => NumExpr) /* */
}
trait GTEInstancesLowPriority2 {
implicit def LinExprGTEOp[A, B](
implicit convA: A => LinExpr, convB: B => LinExpr) /* */
}
> priority
> priority

64. Next steps
New semantics
More static controls
Support other kind of problems beyond MIP
Talk directly to solvers
Arity, scope, ...
Multi-stage stochastic programming

65. import amphip.dsl._import amphip.dsl._
amphip is a collection of
experiments around
manipulating MathProg
models within Scala
github.com/gerferra/amphip
amphip is a collection of
experiments around
manipulating MathProg
models within Scala
github.com/gerferra/amphip

66. FIN
github.com/gerferra/amphip