optimization 0.1.4 → 0.1.5

raw patch · 3 files changed

+12/−9 lines, 3 filesdep ~ad

Dependency ranges changed: ad

Files

• optimization.cabal +2/−2
• src/Optimization/Constrained/Penalty.hs +4/−4
• src/Optimization/LineSearch.hs +6/−3

@@ -1,6 +1,6 @@ name:          optimization category:      Math-version:       0.1.4+version:       0.1.5 license:       BSD3 cabal-version: >= 1.10 license-file:  LICENSE@@ -51,7 +51,7 @@   build-depends:     base                >= 4.4          && < 5,     vector              >= 0.10         && < 1.0,-    ad                  >= 3.4          && < 4.0,+    ad                  >= 3.4          && < 4.3,     linear              >= 1.0          && < 2.0,     semigroupoids       >= 3.0          && < 5.0,     distributive        >= 0.3          && < 0.5

@@ -17,7 +17,7 @@   , lagrangian   ) where -import           Numeric.AD.Types+import           Numeric.AD  import qualified Data.Vector as V @@ -50,7 +50,7 @@  -- | Minimize the given constrained optimization problem -- This is a basic penalty method approach-minimize :: (Functor f, Num a, Ord a, g ~ V)+minimize :: (Functor f, RealFrac a, Ord a, g ~ V)          => (FU f a -> f a -> [f a])   -- ^ Primal minimizer          -> Opt f a                    -- ^ The optimization problem of interest          -> a                          -- ^ The penalty increase factor@@ -59,12 +59,12 @@          -> [f a]                      -- ^ Optimizing iterates minimize minX opt alpha = go   where go x0 l0 = let l1 = fmap (*alpha) l0-                       x1 = head $ drop 100 $ minX (FU $ \x -> augLagrangian opt x (fmap auto l1)) x0+                       x1 = head $ drop 100 $ minX (FU $ \x -> augLagrangian opt x (fmap realToFrac l1)) x0                    in x1 : go x1 l1 {-# INLINEABLE minimize #-}  -- | Maximize the given constrained optimization problem-maximize :: (Functor f, Num a, Ord a, g ~ V)+maximize :: (Functor f, RealFrac a, Ord a, g ~ V)          => (FU f a -> f a -> [f a])   -- ^ Primal minimizer          -> Opt f a                    -- ^ The optimization problem of interest          -> a                          -- ^ The penalty increase factor

@@ -36,6 +36,7 @@  import Prelude hiding (pred) import Linear+import Debug.Trace  -- | A line search method @search df p x@ determines a step size -- in direction @p@ from point @x@ for function @f@ with gradient @df@@@ -63,7 +64,7 @@ {-# INLINE armijo #-}  -- | Curvature condition-curvature :: (Num a, Ord a, Additive f, Metric f)+curvature :: (Num a, Ord a, Additive f, Metric f, Show a, Show (f a))           => a             -- ^ curvature condition strength c2           -> (f a -> f a)  -- ^ gradient of function           -> f a           -- ^ point to evaluate at@@ -71,6 +72,7 @@           -> a             -- ^ search step size           -> Bool          -- ^ is curvature condition satisfied curvature c2 df x p a =+    traceShow (df (x ^+^ a *^ p) `dot` p, c2 * (df x `dot` p), p) $     df (x ^+^ a *^ p) `dot` p >= c2 * (df x `dot` p) {-# INLINE curvature #-} @@ -115,7 +117,7 @@ -- @wolfeSearch gamma alpha c1@ starts with the given step size @alpha@ -- and reduces it by a factor of @gamma@ until both the Armijo and -- curvature conditions is satisfied.-wolfeSearch :: (Num a, Ord a, Metric f)+wolfeSearch :: (Show a, Num a, Ord a, Metric f, Show (f a))              => a                   -- ^ step size reduction factor gamma              -> a                   -- ^ initial step size alpha              -> a                   -- ^ Armijo condition strength c1@@ -124,7 +126,8 @@              -> LineSearch f a wolfeSearch gamma alpha c1 c2 f df p x =     backtrackingSearch gamma alpha wolfe df p x-  where wolfe a = armijo c1 f df p x a && curvature c2 df x p a+  where wolfe a = traceShow (a, armijo c1 f df p x a, curvature c2 df x p a)+                $ armijo c1 f df p x a && curvature c2 df x p a {-# INLINEABLE wolfeSearch #-}  -- | Line search by Newton's method