@ParametricNullness E key,
          List<? extends E> list,
          int foundIndex) {
        // Of course, we have to use binary search to find the precise
        // breakpoint...
        int lower = 0;
        int upper = foundIndex;
        // Of course, we have to use binary search to find the precise breakpoint...
        // Everything between lower and upper inclusive compares at <= 0.
        while (lower < upper) {

  static double getMinValue(double[] array, int from) {
    // This is basically a copy of com.google.math.Rank#getMinValue, with a small change in the
    // method signature: we always search to the end of the array.
    int min = from;
    for (int i = from + 1; i < array.length; i++) {
      if (array[min] > array[i]) {
        min = i;
      }
    }
    return array[min];
  }

  static double getMinValue(double[] array, int from) {
    // This is basically a copy of com.google.math.Rank#getMinValue, with a small change in the
    // method signature: we always search to the end of the array.
    int min = from;
    for (int i = from + 1; i < array.length; i++) {
      if (array[min] > array[i]) {
        min = i;
      }
    }
    return array[min];
  }

    } catch (ClassCastException e) {
      return false;
    }
  }

  @Override
  public boolean containsAll(Collection<?> targets) {
    // TODO(jlevy): For optimal performance, use a binary search when
    // targets.size() < size() / log(size())
    // TODO(kevinb): see if we can share code with OrderedIterator after it
    // graduates from labs.
    if (targets instanceof Multiset) {

   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,
   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of
   * colliding threads.  Because search is random, and collisions
   * only become known via CAS failures, convergence can be slow,

   *
   * @param rowKey key of row to search for
   * @param columnKey key of column to search for
   */
  boolean contains(
      @CompatibleWith("R") @CheckForNull Object rowKey,
      @CompatibleWith("C") @CheckForNull Object columnKey);

  /**
   * Returns {@code true} if the table contains a mapping with the specified row key.
   *
   * @param rowKey key of row to search for
   */

   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,
   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of
   * colliding threads.  Because search is random, and collisions
   * only become known via CAS failures, convergence can be slow,

      return new ScaleAndIndex(scale, index);
    }

    /**
     * Specifies multiple quantile indexes to be calculated, each index being the k in the kth
     * q-quantile.
     *
     * @param indexes the quantile indexes, each of which must be in the inclusive range [0, q] for
     *     q-quantiles; the order of the indexes is unimportant, duplicates will be ignored, and the

   *
   * <pre>{@code
   * Iterables.limit(Traverser.forGraph(graph).breadthFirst(node), maxNumberOfNodes);
   * }</pre>
   *
   * <p>See <a href="https://en.wikipedia.org/wiki/Breadth-first_search">Wikipedia</a> for more
   * info.
   *
   * @throws IllegalArgumentException if {@code startNode} is not an element of the graph
   */
  public final Iterable<N> breadthFirst(N startNode) {

   * i, i + target.length)} contains exactly the same elements as {@code target}.
   *
   * @param array the array to search for the sequence {@code target}
   * @param target the array to search for as a sub-sequence of {@code array}
   */
  public static int indexOf(byte[] array, byte[] target) {
    checkNotNull(array, "array");
    checkNotNull(target, "target");