Thomas Weise (汤卫思)

A professor at Hefei University.

1 minute read

Sometimes, we have the need to delete empty files and empty directories under . This happens, for example, when we want to restart an experiment with . Here I post a small script that you may store as file deleteZeroSizedFiles.sh. It will do exactly that: It will search the current directory and all subdirectories for empty files, i.e., files of size zero. It will delete all of them. Then, it will recursively look for empty directories, i.e., directories that do not contain files or other directories. It will delete them as well.

#!/bin/bash

# Turn on strict error handling, so that the script fails as soon as something goes wrong.
set -o pipefail  # trace ERR through pipes
set -o errtrace  # trace ERR through 'time command' and other functions
set -o nounset   # set -u : exit the script if you try to use an uninitialized variable
set -o errexit   # set -e : exit the script if any statement returns a non-true return value

echo "$(date +'%0Y-%0m-%0d %0R:%0S'): Deleting all empty files in current directory recursively."
find . -type f -empty -delete

echo "$(date +'%0Y-%0m-%0d %0R:%0S'): Deleting all empty directories in current directory recurisvely."
find . -type d -empty -delete

echo "$(date +'%0Y-%0m-%0d %0R:%0S'): Finished cleaning up."

You May Also Enjoy

Repeating a Command until it Succeeds under Linux

2 minute read

Assume that we have a command that we want to execute in a terminal. We know that the command may sometimes fail, but it should actually succeed. So we would try the command again and again until this happens. An example is, for instance, a download process. If we know that the URL is reachable, then the command should succeed. However, maybe there is a lost connection or other temporary disturbance. Another such command could be a build process which downloads and installs dependencies.

Plugging Frequency Fitness Assignment into Metaheuristics

12 minute read

Frequency Fitness Assignment (, 频率适应度分配) is a technique that fundamentally changes how (metaheuristic) optimization algorithms work. The goal of this post is to explore how this technique can be plugged into an existing algorithm. We first discuss optimization and the general pattern of metaheuristics in general. We then discuss the simplest local search algorithm – randomized local search, or for short. We plug FFA into this algorithm and obtain the . We finally list some properties of FRLS as well as some related works.