diff --git a/CHANGELOG.md b/CHANGELOG.md
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--- /dev/null
+++ b/CHANGELOG.md
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+# 0
+* Written
diff --git a/LICENSE.md b/LICENSE.md
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--- /dev/null
+++ b/LICENSE.md
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+# License
+
+Licensed under either of
+ * Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+ * BSD 2-Clause license (https://opensource.org/licenses/BSD-2-Clause)
+at your option.
+
+## BSD 2-Clause License
+
+- Copyright 2019 Edward Kmett and Sean Chalmers
+
+All rights reserved.
+
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+POSSIBILITY OF SUCH DAMAGE.
+
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diff --git a/README.md b/README.md
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+++ b/README.md
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+smawk
+=====
+
+[![Hackage][hackage-shield]][hackage]
+
+This package implements the SMAWK algorithm by
+[Peter Shor, Shlomo Moran, Alok Aggarwal, Robert Wilber and Maria Klawe][smawk]
+for finding the minimum value in each row of an implicitly defined totally monotone matrix.
+
+This has many applications in computational geometry, such as finding the farthest point
+from each point in a convex polygon, finding optimal enclosing polygon. It can also be
+used to implement paragraph line breaking in a manner analogous to Knuth and Platt, but in
+linear time. It also has uses in RNA secondary structure prediction, various sequence
+alignment problems, construction of prefix codes, image thresholding, etc.
+
+Contact Information
+-------------------
+
+Contributions and bug reports are welcome!
+
+Please feel free to contact me through github or on the #haskell IRC channel on irc.freenode.net.
+
+-Edward Kmett
+
+[smawk]: https://link.springer.com/article/10.1007%2FBF01840359
+[hackage]: https://hackage.haskell.org/package/smawk
+[hackage-shield]: https://img.shields.io/hackage/v/smawk.svg
diff --git a/smawk.cabal b/smawk.cabal
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--- /dev/null
+++ b/smawk.cabal
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+cabal-version: 2.2
+name: smawk
+version: 0
+synopsis: Linear time row minima for totally monotone matrices
+homepage: https://github.com/ekmett/codex/tree/master/smawk#readme
+license: BSD-2-Clause OR Apache-2.0
+license-file: LICENSE.md
+author: Edward Kmett
+maintainer: Edward Kmett <ekmett@gmail.com>
+copyright: Copyright (c) 2019 Edward Kmett
+stability: experimental
+category: FFI
+build-type: Simple
+description:
+  This implements the SMAWK algorithm by Peter Shor, Shlomo Moran,
+  Alok Aggarwal, Robert Wilber and Maria Klawe for finding the minimum
+  value in each row of an implicitly defined totally monotone matrix.
+  .
+  This has many applications in computational geometry, such as finding
+  the farthest point from each point in a convex polygon, finding
+  optimal enclosing polygon. It can also be used to implement paragraph
+  line breaking in a manner analogous to Knuth and Platt, but in linear
+  time. It also has uses in RNA secondary structure prediction, various
+  sequence alignment problems, construction of prefix codes, image
+  thresholding, etc.
+extra-doc-files:
+  README.md,
+  CHANGELOG.md
+
+source-repository head
+  type: git
+  location: https://github.com/ekmett/codex
+  subdir: smawk
+
+library
+  hs-source-dirs: src
+  default-language: Haskell2010
+  ghc-options:
+    -Wall -Wincomplete-uni-patterns -Wincomplete-record-updates
+    -Wredundant-constraints -Widentities -Wmissing-export-lists
+  build-depends:
+    , base >= 4.11 && < 5
+    , primitive ^>= 0.7
+    , semigroupoids >= 5 && < 6
+    , transformers ^>= 0.5.5
+  exposed-modules:
+    Data.Smawk
diff --git a/src/Data/Smawk.hs b/src/Data/Smawk.hs
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--- /dev/null
+++ b/src/Data/Smawk.hs
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+{-# options_ghc -Wno-incomplete-uni-patterns #-}
+
+-- | This module implements a generalized version of the SMAWK algorithm
+-- for computing row minima in totally ordered matrices.
+--
+-- I do not require rows or column numbers to be actual numbers, or even ordered,
+-- instead comparing columns using occurrence order.
+--
+-- Unlike @Map@-based implementations, the runtime of this is actually linear.
+module Data.Smawk
+  ( smawk
+  , smawk1
+  ) where
+
+import Control.Monad.Trans.State.Strict
+import qualified Data.Foldable as Foldable
+import Data.Maybe (fromJust)
+import Data.Semigroup (Min(..),Arg(..))
+import Data.Semigroup.Foldable (Foldable1)
+import Data.List.NonEmpty (nonEmpty)
+import Data.Primitive.Array (indexArray)
+import GHC.Exts as Exts
+
+-- |
+-- >>> collate "abcde"
+-- ("ace","bd")
+collate :: [a] -> ([a],[a])
+collate = Prelude.foldr (\a ~(x,y) -> (a:y,x)) mempty
+
+-- |
+-- @'uncurry' 'interleave' . 'collate' = id@
+--
+-- >>> interleave "ace" "bd"
+-- "abcde"
+interleave :: [a] -> [a] -> [a]
+interleave (a:as) bs = a:interleave bs as
+interleave [] bs = bs
+
+-- |
+-- /O(|rows| + |cols|)/.
+--
+-- Computes __row__ minima in totally monotone matrices using the SMAWK algorithm.
+--
+-- Returns 'Nothing' if we have no columns.
+smawk
+  :: (Traversable f, Foldable g, Ord a)
+  => f r -- ^ rows (in any desired ascending order)
+  -> g c -- ^ columns (in any desired ascending order)
+  -> (r -> c -> a) -- ^ a monotone matrix
+  -> Maybe (f c) -- ^ each of the row minima
+smawk rs cs0 m = (\cs -> smawk1 rs cs m) <$> nonEmpty (Foldable.toList cs0)
+{-# inline smawk #-}
+
+-- |
+-- /O(|rows| + |cols|)/.
+--
+-- Computes __row__ minima in totally monotone matrices using the SMAWK algorithm.
+smawk1
+  :: (Traversable f, Foldable1 g, Ord a)
+  => f r -- ^ rows (in any desired ascending order)
+  -> g c -- ^ columns (in any desired ascending order)
+  -> (r -> c -> a) -- ^ a monotone matrix
+  -> f c -- ^ each of the row minima
+smawk1 rs0 cs0 m = evalState (traverse refill rs0) $ go (Foldable.toList rs0) [0..length raws-1] where
+  raws = Exts.fromList $ Foldable.toList cs0
+  refill _ = state $ \ ~(x:xs) -> (indexArray raws x,xs)
+  go [] _ = []
+  go rs cs = interleave broken minima where
+    m' r c = m r (indexArray raws c)
+    broken = zipWith skim es $ path cs' (minima <> repeat l)
+    -- skim a bs = snd $ minimum $ fmap (\i -> (m i a, i)) bs
+    skim a bs = case getMin $ fromJust $ foldMap (\i -> Just $ Min $ Arg (m' a i) i) bs of Arg _ i -> i
+    path xs ~(y:ys) = case span (<=y) xs of
+      (as, bs) -> as:path (y:bs) ys
+    rs' = Exts.fromList rs -- a zipper yields same complexity, worst constants
+    n = length rs'
+    minima = go os cs'
+    (es,os) = collate rs
+    rcs' = reduce cs [] 0
+    cs' = reverse rcs'
+    l = head rcs'
+    reduce [] ys _ = ys
+    reduce (x:xs) [] _ = reduce xs [x] 1
+    reduce xxs@(x:xs) yys@(y:ys) t
+      | ri <- indexArray rs' (t-1), m' ri y > m' ri y = reduce xxs ys (t-1)
+      | t /= n = reduce xs (x:yys) (t+1)
+      | otherwise = reduce xs yys t
+{-# inlinable smawk1 #-}