Bump SBT version

project/build.properties

@@ -1 +1 @@
-sbt.version = 0.13.13
+sbt.version = 1.0.4

project/plugins.sbt

@@ -1,2 +1,2 @@
 logLevel := Level.Warn
-addSbtPlugin("org.scalariform" % "sbt-scalariform" % "1.7.1")
+addSbtPlugin("org.scalariform" % "sbt-scalariform" % "1.8.2")

src/main/scala/org/pfcoperez/dailyalgorithm/Geometry.scala

@@ -88,14 +88,14 @@ object Geometry {
     }
 
   /**
-    * Minkowski addition for convex polygons.
-    * O(n+m), n = number of vertices in polygon `a`, m = number of vertices in polygon 'b'
-    * 
-    * NOTE: The Minkowski addition time complexity is O(m+m), however, this
-    *       implementation makes sure its inputs are convex polygons, therefore, as long
-    *       as these inputs are actually convex hulls, the actual time complexity is O(n^2 + m^2).
-    *       If the initial check is removed, the time complexity will automatically become O(n+m)
-    */
+   * Minkowski addition for convex polygons.
+   * O(n+m), n = number of vertices in polygon `a`, m = number of vertices in polygon 'b'
+   *
+   * NOTE: The Minkowski addition time complexity is O(m+m), however, this
+   *       implementation makes sure its inputs are convex polygons, therefore, as long
+   *       as these inputs are actually convex hulls, the actual time complexity is O(n^2 + m^2).
+   *       If the initial check is removed, the time complexity will automatically become O(n+m)
+   */
   def convexMinkowskiAddition(a: Set[Point], b: Set[Point]): Option[List[Point]] =
     for {
       convexPolygonA <- fasterGiftWrappingConvexHull(a)

src/main/scala/org/pfcoperez/dailyalgorithm/cstheory/StateAutomata.scala

@@ -47,7 +47,7 @@ object StateAutomata {
      * O(n + m), n = number of states, m = number of transitions
      */
     def mergeStates(a: State, b: State): Automaton[State, Entry, Output] =
-      if(a == b) this
+      if (a == b) this
       else {
         val redirectedTransitions = transitionMatrix mapValues {
           _.mapValues {
@@ -85,9 +85,9 @@ object StateAutomata {
   object Automaton {
 
     /**
-      * Produce a minimized version, through Myhill-Nerode theorem, of the input automaton.
-      * O(n^2 m^2), n = number of states, m = number of transitions.
-      */
+     * Produce a minimized version, through Myhill-Nerode theorem, of the input automaton.
+     * O(n^2 m^2), n = number of states, m = number of transitions.
+     */
     def minimize[State, Entry, Output](automaton: Automaton[State, Entry, Output]): Automaton[State, Entry, Output] = {
       val indexedStates = automaton.states.toVector
       def isFinal(st: State): Boolean = automaton.finalStates contains st