lagrangian 0.3.0.0 → 0.3.0.1

raw patch · 3 files changed

+8/−9 lines, 3 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• lagrangian.cabal +2/−2
• src/Numeric/AD/Lagrangian.hs +4/−5
• src/Numeric/AD/Lagrangian/Internal.hs +2/−2

@@ -10,7 +10,7 @@ -- PVP summary:      +-+------- breaking API changes --                   | | +----- non-breaking API additions --                   | | | +--- code changes with no API change-version:             0.3.0.0+version:             0.3.0.1  -- A short (one-line) description of the package. synopsis:            Solve lagrange multiplier problems@@ -24,7 +24,7 @@  .  @    \> solve 0.00001 (negate . sum . map (\x -> x * log x)) [sum \<=\> 1] 3-   ([0.33, 0.33, 0.33], [-0.09])+   Right ([0.33, 0.33, 0.33], [-0.09])  @  .  The first elements of the result pair are the arguments for the 

@@ -1,11 +1,10 @@ -- |Numerically solve convex lagrange multiplier problems with conjugate gradient descent.  --  ---  For example, find the maximum entropy with the constraint that the probabilities add---  up to one. ---  +--  Here is an example from the Wikipedia page on Lagrange multipliers.+--  Maximize f(x, y) = x + y, subject to the constraint x^2 + y^2 = 1  --  ---  >>> solve 0.00001 (negate . sum . map (\x -> x * log x)) [sum <=> 1] 3---  ([0.33, 0.33, 0.33], [-0.09])+--  >>> solve 0.00001 (\[x, y] -> x + y) [\[x, y] -> x^2 + y^2 <=> 1] 2+--  Right ([0.707,0.707], [-0.707]) --   --  The first elements of the result pair are the arguments for the objective function at the minimum.  --  The second elements are the lagrange multipliers.

@@ -37,7 +37,7 @@       -- ^ The arity of the objective function which should equal the arity of        --   the constraints.       -> Either (Result, Statistics) (S.Vector Double, S.Vector Double) -      -- ^ Either an explaination of why the gradient descent failed or a pair +      -- ^ Either an explanation of why the gradient descent failed or a pair        --   containing the arguments at the minimum and the lagrange multipliers solve tolerance toMin constraints argCount = result where     -- The function to minimize for the langrangian is the squared gradient@@ -85,7 +85,7 @@  -- | WARNING. Experimental. --   This is not a true feasibility test for the function. I am not sure ---   exactly how to implement that. This just checks the feasiblility at point.+--   exactly how to implement that. This just checks the feasiblility at a point. --   If this ever returns false, 'solve' can fail. feasible :: (forall s r. (Mode s, Mode r) => [AD2 s r Double] -> AD2 s r Double)          -> (forall s r. (Mode s, Mode r) => [Constraint (AD2 s r Double)] )