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Well, my experience is in mathematica. Which has operations research functions built right in. Point being, there are systems where this is already a solve prob

Well, my experience is in mathematica. Which has operations research functions built right in. Point being, there are systems where this is already a solve problem, where you can get your answer in a minute or two. So you'd simply do Minimize[function, {variables/constraints}] and then get on with your day.

yummyfajitas 14y ago
It's true, glpk is not part of the haskell standard library. But solving a simple LP problem is easily done in a minute or two: lp = execLPM $ do setDirection Max setObjective objFun leqTo (varSum ["x1", "x2", "x3"]) 100 leqTo (10 *^ var "x1" ^+^ 4 *& "x2" ^+^ 5 *^ var "x3") 600 leqTo (linCombination [(2, "x1"), (2, "x2"), (6, "x3")]) 300 varGeq "x1" 0 varBds "x2" 0 50 varGeq "x3" 0 setVarKind "x1" IntVar setVarKind "x2" ContVar main = print =<< glpSolveVars mipDefaults lp The code in the blog post is just longer and more complicated because it's generating multiple constraints from code. The only reason my code doesn't look as simple as the above is because it's doing a lot more. For example, this line generates 9 constraints: reservationConstraints loadPattern = [ linCombination [ (1.0, "reservation_" ++ k), (-1.0, "reserved_" ++ k ++ "_" ++ p) ], 0.0 | p <- (Map.keys loadPattern), k <- reservationTypes ] E.g.: reservation_heavy - reserved_heavy_morning <= 0 reservation_heavy - reserved_heavy_afternoon <= 0 reservation_heavy - reserved_heavy_evening <= 0 reservation_medium - reserved_medium_evening <= 0 .... If Mathematica is so easy, I'd love to see a significantly simpler version of the same problem written in it. But I have absolutely no idea how I'd write that simpler version.
- nukedukem 14y ago
  Maximize[...], Minimize[...], LinearProgramming[...] thats it... But Mathematica costs money. Example: Minimize[{x + 2 y, -5 x + y == 7 && x + y >= 26 && x >= 3 && y >= 4}, {x, y}] LinearProgramming[{1, 2}, {{-5, 1}, {1, 1}}, {{7, 0}, {26, 1}}, {{3, Infinity}, {4, Infinity}}]
  - yummyfajitas 14y ago
    The vast majority of the code I wrote is setting up the problem (which has 15 vars and 27 constraints), not solving it. I.e., those list comprehensions are a way of writing 9 constraints on a single line. To demonstrate mathematica is easier, you need to demonstrate that it's similarly easy to translate the problem from a verbal specification to an LP problem. Solving LP is the easy part.

yummyfajitas 14y ago

It's true, glpk is not part of the haskell standard library. But solving a simple LP problem is easily done in a minute or two: lp = execLPM $ do setDirection Max setObjective objFun leqTo (varSum ["x1", "x2", "x3"]) 100 leqTo (10 *^ var "x1" ^+^ 4 *& "x2" ^+^ 5 *^ var "x3") 600 leqTo (linCombination [(2, "x1"), (2, "x2"), (6, "x3")]) 300 varGeq "x1" 0 varBds "x2" 0 50 varGeq "x3" 0 setVarKind "x1" IntVar setVarKind "x2" ContVar main = print =<< glpSolveVars mipDefaults lp The code in the blog post is just longer and more complicated because it's generating multiple constraints from code. The only reason my code doesn't look as simple as the above is because it's doing a lot more. For example, this line generates 9 constraints: reservationConstraints loadPattern = [ linCombination [ (1.0, "reservation_" ++ k), (-1.0, "reserved_" ++ k ++ "_" ++ p) ], 0.0 | p <- (Map.keys loadPattern), k <- reservationTypes ] E.g.: reservation_heavy - reserved_heavy_morning <= 0 reservation_heavy - reserved_heavy_afternoon <= 0 reservation_heavy - reserved_heavy_evening <= 0 reservation_medium - reserved_medium_evening <= 0 .... If Mathematica is so easy, I'd love to see a significantly simpler version of the same problem written in it. But I have absolutely no idea how I'd write that simpler version.

nukedukem 14y ago
Maximize[...], Minimize[...], LinearProgramming[...] thats it... But Mathematica costs money. Example: Minimize[{x + 2 y, -5 x + y == 7 && x + y >= 26 && x >= 3 && y >= 4}, {x, y}] LinearProgramming[{1, 2}, {{-5, 1}, {1, 1}}, {{7, 0}, {26, 1}}, {{3, Infinity}, {4, Infinity}}]
- yummyfajitas 14y ago
  The vast majority of the code I wrote is setting up the problem (which has 15 vars and 27 constraints), not solving it. I.e., those list comprehensions are a way of writing 9 constraints on a single line. To demonstrate mathematica is easier, you need to demonstrate that it's similarly easy to translate the problem from a verbal specification to an LP problem. Solving LP is the easy part.