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Git Tutorial: 10 Common Git Problems and How to Fix Them

Learning Git? This Git tutorial covers the 10 most common Git tricks you should know about: how to undo commits, revert commits, edit commit messages, discard local files, resolve merge conflicts, and more.

1. Discard local file modifications

Sometimes the best way to get a feel for a problem is diving in and playing around with the code. Unfortunately, the changes made in the process sometimes turn out to be less than optimal, in which case reverting the file to its original state can be the fastest and easiest solution:

In case you’re wondering, the double dash ( -- ) is a common way for command line utilities to signify the end of command options.

2. Undo local commits

Alas, sometimes it takes us a bit longer to realize that we are on the wrong track, and by that time one or more changes may already have been committed locally. This is when git reset comes in handy:

Be careful with the --hard option! It resets your working tree as well as the index, so all your modifications will be lost for good.

3. Remove a file from git without removing it from your file system

If you are not careful during a git add , you may end up adding files that you didn’t want to commit. However, git rm will remove it from both your staging area, as well as your file system, which may not be what you want. In that case make sure you only remove the staged version, and add the file to your .gitignore to avoid making the same mistake a second time:

4. Edit a commit message

Typos happen, but luckily in the case of commit messages, it is very easy to fix them:

But that’s not all git-amend can do for you. Did you forget to add a file? Just add it and amend the previous commit!

Please keep in mind that --amend actually will create a new commit which replaces the previous one, so don’t use it for modifying commits which already have been pushed to a central repository. An exception to this rule can be made if you are absolutely sure that no other developer has already checked out the previous version and based their own work on it, in which case a forced push ( git push --force ) may still be ok. The --force option is necessary here since the tree’s history was locally modified which means the push will be rejected by the remote server since no fast-forward merge is possible.

5. Clean up local commits before pushing

While --amend is very useful, it doesn’t help if the commit you want to reword is not the last one. In that case an interactive rebase comes in handy:

This will open your configured editor and present you with the following menu:

On top you’ll see a list of local commits, followed by an explanation of the available commands. Just pick the commit(s) you want to update, change pick to reword (or r for short), and you will be taken to a new view where you can edit the message.

However, as can be seen from the above listing, interactive rebases offer a lot more than simple commit message editing: you can completely remove commits by deleting them from the list, as well as edit, reorder, and squash them. Squashing allows you to merge several commits into one, which is something I like to do on feature branches before pushing them to the remote. No more “Add forgotten file” and “Fix typo” commits recorded for eternity!

6. Reverting pushed commits

Despite the fixes demonstrated in the previous tips, faulty commits do occasionally make it into the central repository. Still this is no reason to despair, since git offers an easy way to revert single or multiple commits:

In case you don’t want to create additional revert commits but only apply the necessary changes to your working tree, you can use the --no-commit / -n option.

The manual page at man 1 git-revert list further options and provides some additional examples.

7. Avoid repeated merge conflicts

As every developer knows, fixing merge conflicts can be tedious, but solving the exact same conflict repeatedly (e.g. in long running feature branches) is outright annoying. If you’ve suffered from this in the past, you’ll be happy to learn about the underused reuse recorded resolution feature. Add it to your global config to enable it for all projects:

Alternatively you can enable it on a per-project basis by manually creating the directory .git/rr-cache .

This sure isn’t a feature for everyone, but for people who need it, it can be real time saver. Imagine your team is working on various feature branches at the same time. Now you want to merge all of them together into one testable pre-release branch. As expected, there are several merge conflicts, which you resolve. Unfortunately it turns out that one of the branches isn’t quite there yet, so you decide to un-merge it again. Several days (or weeks) later when the branch is finally ready you merge it again, but thanks to the recorded resolutions, you won’t have to resolve the same merge conflicts again.

The man page ( man git-rerere ) has more information on further use cases and commands ( git rerere status , git rerere diff , etc).

8. Find the commit that broke something after a merge

Tracking down the commit that introduced a bug after a big merge can be quite time consuming. Luckily git offers a great binary search facility in the form of git-bisect . First you have to perform the initial setup:

After this git will automatically checkout a revision halfway between the known “good” and “bad” versions. You can now run your specs again and mark the commit as “good” or “bad” accordingly.

This process continues until you get to the commit that introduced the bug.

9. Avoid common mistakes with git hooks

Some mistakes happen repeatedly, but would be easy to avoid by running certain checks or cleanup tasks at a defined stage of the git workflow. This is exactly the scenario that hooks were designed for. To create a new hook, add an executable file to .git/hooks . The name of the script has to correspond to one of the available hooks, a full list of which is available in the manual page ( man githooks ). You can also define global hooks to use in all your projects by creating a template directory that git will use when initializing a new repository (see man git-init for further information). Here’s how the relevant entry in ~/.gitconfig and an example template directory look like:

When you initialize a new repository, files in the template directory will be copied to the corresponding location in your project’s .git directory.

What follows is a slightly contrived example commit-msg hook, which will ensure that every commit message references a ticket number like “ #123 “.

10. When all else fails

So far we covered quite a lot of ground on how to fix common errors when working with git . Most of them have easy enough solutions, however there are times when one has to get out the big guns and rewrite the history of an entire branch. One common use case for this is removing sensitive data (e.g. login credentials for production systems) that were committed to a public repository:

This will remove the file secrets.txt from every branch and tag. It will also remove any commits that would be empty as a result of the above operation. Keep in mind that this will rewrite your project’s entire history, which can be very disruptive in a distributed workflow. Also while the file in question has now been removed, the credentials it contained should still be considered compromised!

GitHub has a very good tutorial on removing sensitive data and man git-filter-branch has all details on the various available filters and their options.

Enjoy this post? Give Michael Kohl a like if it's helpful.

Leave a like and comment for Michael

I’m plagued by merges that embed

HEAD = = = = = = = branch

directly into my code thus breaking it.

Any idea what process I might be doing wrong that causes this and how best to avoid?

Small typo in 3 ponit echo filename >> .gitingore

One of the best use case scenarios defined…Thank you for the help!!!

Glad it helped :)

How to Resolve Merge Conflicts in Git – A Practical Guide with Examples

Git is an open-source distributed version control system. It helps you manage your project files easily using local branching, staging, and workflows.

Many developers use Git today. And they're usually familiar with basic Git concepts like:

How to initiate a repository.
How to create branches.
How to stage/unstage changes.
How to commit changes.
How to push commits to remote.

However, many developers are confused about concepts like merging and resolving merge conflicts . In this article, we will learn how to resolve merge conflicts in a practical way. This means you will read, understand, and try it out while going through this article.

If you like to learn from video content as well, this article is also available as a video tutorial here: 🙂

If you are new to Git and want to learn all the basic concepts, here is a helpful crash course .

What are Devs Saying about "Merge Conflicts"?

Recently I conducted a poll on Twitter, LinkedIn, and YouTube, asking if developers are comfortable with resolving merge conflicts in Git. Guess what I found?

70%-80% of developers shared that they find it challenging to resolve a merge conflict in Git. So this means that "Resolving Merge Conflicts" is an important topic of discussion.

What is Git Merge and What are Merge Conflicts?

Git is a version control system that keeps a history of all your file versions. You can go back to any of the versions at any time and retrieve an older version.

Suppose you have created a file called abc.txt and pushed it to a Git repository. At this point, the file has its current version associated with it. Now, if your co-worker changed the same file and pushed it back to the repository, the file has a new version associated.

Git Merge is a feature that allows you to keep the file's current content in sync with other previous versions. This is essential because anyone at any point in time should be working on the most recent content of the file without overriding any changes from the previous versions.

Git merge helps you merge changes from other developers before pushing a new change to the same file.

In the case of Git merge, we need to be aware of two things:

Changes : What type of operations occurred between two versions of a file? New content is added or removed, or existing content is updated.
Possibilities : There are two possibilities. The changes happened in the different regions of the file or the changes happened in the same region of the file. Same region means that developers have made changes around the same place (for example, paragraphs, lines, and so on) of a file.

Fortunately, Git automatically takes care of most of these cases using the auto-merge strategy. But when the changes have occurred in the same region of the file, Git won't perform an auto-merge. Instead, it leaves it to you to Resolve the Merge Conflicts .

Git Merge Conflicts: A Horror Story

Let's understand the above situations with a story of two developers, Alex and Tina.

One fine day,

Alex pulled changes from the remote repository to his local repository.
He changed the file called abc.txt , staged it, committed it, and finally pushed it back to the remote repository.
In the meantime, Tina, unaware of Alex's changes in the abc.txt file, made some changes in the same region of the file and tried pushing it to the remote repository.
Git is a version control system, so it warned Tina that she had changed the version older than what it was in the remote (as Alex's changes were already in the remote).
Now, Tina needs to first pull the changes from the remote, update the file, and then try pushing again.
Tina did this. However, in her wildest nightmare, she got the warning that auto-merge failed, and so she needs to now Resolve the merge conflicts .

Does this story ring any bells? Is the above story related to you? There's a chance that you've been in Tina's shoes in the past. If not, you will get there eventually! So, let's understand how Tina has to deal with this situation efficiently.

How to Resolve Merge Conflicts in Git

Resolving merge conflicts is not as tricky as it may sound. In 90% of cases, it is easier once you have a clear understanding of the changes and a peaceful mind.

Thought Process

Once Tina pulls the changes, Tina's local file has her changes plus Alex's changes. Now Tina can do one of these four things:

She can keep Alex's changes and remove hers.
She can remove Alex's changes and keep hers.
She can keep both Alex's and her changes.
She can remove both Alex's and her changes.

Alright, but which one she should be doing? That is entirely dependent on the project's needs and the use-cases. Tina will understand the incoming changes and do whatever is relevant to the situation.

So, what are incoming changes? How's Tina going to identify that? How does Tina make the changes? I know you have got many such questions. Let's get the answers to all of them by taking a couple of real-life examples in the section below.

Steps to Resolve Merge Conflicts in Git

Let's take a couple of real-life examples of merge conflicts, and learn how to resolve them.

At any point in time, if you want to learn these concepts interactively, please check out this section of the video I have mentioned at the beginning of this article.

Example 1: Changes are in the Same Region of the File

When Git cannot perform an auto-merge because changes are in the same region, it indicates the conflicting regions with special characters. The character sequences are like this:

<<<<<<<
>>>>>>>

Everything between <<<<<<< and ======= are your local changes. These changes are not in the remote repository yet. All the lines between ======= and >>>>>>> are the changes from the remote repository or another branch. Now you need to look into these two sections and make a decision.

The image below shows the content of a file indicating that auto-merge didn't occur and there is a conflict. The conflict is in the line where we have modified the file locally by adding a line - Sleep . But in the meantime, someone else pushed a change by adding the line - Gym in the same region.

So, the line - Sleep is marked as the local change and - Gym as the incoming changes from the remote repository or another branch.

Based on your use case and project needs, you will make the call to resolve the conflict. If you need to keep only the line with - Sleep , you will keep that and remove the rest of the conflicting texts. In that case, the file content becomes:

On the contrary, you can keep the line - Gym and remove the - Sleep changes:

If you need to keep both lines, remove the lines related to the conflict indicators:

If you think none of the changes are required, remove them all.

It is entirely up to you to decide what changes are relevant to the situation. After your changes, you need to make sure that none of the conflict-indicating characters exist (<<<<<<<, =======, >>>>>>>) in the file. Once you settle with the changes, do the following:

Stage the changes:

Commit the changes with a message:

Finally, push the changes to the remote:

That's all there is to it to resolve the merge conflict in this scenario.

Example 2: The File is Removed at the Remote/Other Branch

In removed file merge conflicts, a dev deletes a file in one branch while another dev edits the same file in another branch. In this case, you need to decide if you want to keep the file or if it was right to delete it.

To add the deleted file back to your branch, do this:

To go ahead with the deletion of the file, do this:

Then commit your changes with a message:

Finally, push it:

What's Next?

If you learn from the above two examples and practice them, you will be able to take care of most scenarios and resolve your merge conflicts. So, I recommend practicing them a couple of times.

If you face any new scenarios or get stuck in resolving a merge conflict, feel free to post a comment about it in the comment section of this video . I'll try my best to respond back!

Before we wrap up, a few tips for you:

All the examples shown in this article assumes that you're using GitBash or any other Git CLI to resolve merge conflicts. You can use any other GUI tool to do the same.
Always pull from remote/other related branches before you start any new logical work on your code. It will keep your branch up-to-date as much as possible and reduce the chances of conflicts.
Always pull before a push to make sure you will not face any rejections from Git.
Talk to your peers/co-developers when you are unable to make a call on what to keep vs. what to remove. Pair up to resolve any difficult merge conflicts.

That's all for now. I hope you found this article informative and insightful to help you with merge conflicts in Git.

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Solving Generalized Assignment Problem using Branch-And-Price

Oct 27, 2021

Generalized assignment problem, dantzig-wolfe decomposition, column generation, standalone model, initial solution, branching rule, branch-and-price.

One of the best known and widely researched problems in combinatorial optimization is Knapsack Problem:

given a set of items, each with its weight and value,
select a set of items maximizing total value
that can fit into a knapsack while respecting its weight limit.

The most common variant of a problem, called 0-1 Knapsack Problem can be formulated as follows:

$m$ - number of items;
$x_i$ - binary variable indicating whether item is selected;
$v_i$ - value of each items;
$w_i$ - weight of each items;
$W$ - maximum weight capacity.

As often happens in mathematics, or science in general, an obvious question to ask is how the problem can be generalized. One of generalization is Generalized Assignment Problem. It answers question - how to find a maximum profit assignment of $m$ tasks to $n$ machines such that each task ($i=0, \ldots, m$) is assigned to exactly one machine ($j=1, \ldots, n$), and one machine can have multiple tasks assigned to subject to its capacity limitation. Its standard formulation is presented below:

$n$ - number of machines;
$m$ - number of tasks;
$x_{ij}$ - binary variable indicating whether task $i$ is assigned to machine $j$;
$v_{ij}$ - value/profit of assigning task $i$ to machine $j$;
$w_{ij}$ - weight of assigning task $i$ to machine $j$;
$c_j$ - capacity of machine $j$.

Branch-and-price

Branch-and-price is generalization of branch-and-bound method to solve integer programs (IPs),mixed integer programs (MIPs) or binary problems. Both branch-and-price, branch-and-bound, and also branch-and-cut, solve LP relaxation of a given IP. The goal of branch-and-price is to tighten LP relaxation by generating a subset of profitable columns associated with variables to join the current basis.

Branch-and-price builds at the top of branch-and-bound framework. It applies column generation priori to branching. Assuming maximization problem, branching occurs when:

Column Generation is finished (i.e. no profitable columns can be found).
Objective value of the current solution is greater than best lower bound.
The current solution does not satisfy integrality constraints.

However, if only first two conditions are met but not the third one, meaning the current solution satisfies integrality constraints, then the best solution and lower bound are updated (lower bound is tightened) with respectively the current solution and its objective value.

The crucial element needed to apply branch-and-price successfully is to find branching scheme. It is tailored to specific problem to make sure that it does not destroy problem structure and can be used in pricing subproblem to effectively generate columns that enter Restricted Master Problem (RMP) while respecting branching rules .

Below is flow diagram describing branch-and-price method:

The successful application B&P depends on tight/strong model formulation. Model formulation is considered tight if solution of its LP relaxation satisfies (frequently) integrality constraints. One of structured approaches to come up with such a formulation is to use Dantzig-Wolfe Decomposition technique. We will see example of it applied to Generalized Assignment Problem (GAP).

A standard formulation was described above. Now, let’s try to reformulate problem. Let

be a set containing all feasible solutions to Knapsack problem for $j$-th machine. Clearly, $S_j$ contains finite number of points, so $S_j = \{ \mathbf{z}_j^1, \ldots, \mathbf{z}_j^{K_j} \}$, where $\mathbf{z}_j^k \in \{0, 1\}^{m}$. You can think about $\mathbf{z}_j^k \in \{0, 1\}^{m}$ as 0-1 encoding of tasks that form $k$-th feasible solution for machine $j$. Now, let $S = \{ \mathbf{z}_1^1, \ldots, \mathbf{z}_1^{K_1}, \ldots, \mathbf{z}_n^1, \ldots, \mathbf{z}_n^{K_n} \}$ be a set of all feasible solution to GAP. It, potentially, contains a very large number of elements. Then, every point $x_{ij}$ can be expressed by the following convex combination:

where $z_{ij}^k \in \{0, 1\}$, and $z_{ij}^k = 1$ iff task $i$ is assigned to machine $j$ in $k$-th feasible solution for the machine.

Now, let’s use this representation to reformulate GAP:

Note that we do not need capacity restrictions as they are embedded into definition of feasible solution for machine $j$.

Now that we have formulation that is suitable for column generation, let’s turn our attention to it.

Column generation is another crucial component of branch-and-price. There are many great resources devoted to column generation so I will mention only core points:

Column generation is useful when a problem’s pool of feasible solutions contains many elements but only small subset will be present in the optimal solution.
There exists subproblem (called often pricing problem) that can be used to effectively generate columns that should enter RMP.
Column generation starts with initial feasible solution.
Pricing subproblem objective function is updated with dual of the current solution.
Columns with positive reduced cost, in case of maximization problem, enter problem.
Procedure continues until such columns exist.

Below is flow diagram describing column generation method:

Implementation

Let’s see how one can approach implementation of B&P to solve Generalized Assignment Problem. Below is discussion about main concepts and few code excerpts, a repository containing all code can be found on github .

An example of problem instance taken from [1] is:

It is always good idea to have a reference simple(r) implementation that can be used to validate our results using more sophisticated methods. In our case it is based on standard problem formulation. Implementation can be found in repo by checking classes GAPStandaloneModelBuilder and GAPStandaloneModel . Formulation for a problem instance presented above looks as follows:

Now let’s try to examine building blocks of B&P to discus main part at the end, once all the puzzles are there.

To start column generation process, we need to have an initial solution. One possible way to derive it is to use two-phase Simplex method. In first step, you add slack variables to each constraint and set objective function as their sum. Then you minimize the problem. If your solution has objective value $0$, then first of all you have initial solution and you know that your problem is feasible. In case you end up with positive value for any of slack variables, you can conclude that the problem is infeasible. You can stop here.

I took a different approach and came up with simple heuristic that generate initial solution. I have not analyzed it thoroughly so I am not sure if it is guaranteed to always return feasible solution if one exists. Its idea is quite simple:

Construct bipartite graph defined as $G=(V, A)$, where $V = T \cup M$ – $T$ is set of tasks and obviously $M$ is set of machines. There exists arc $a = (t, m)$ if $w_{tm} \le rc_{m}$, where $rc_{m}$ is remaining capacity for machine $m$. Initially remaining capacity is equal to capacity of machine and with each iteration, and assignment of task to machine it is being update. If $\vert A \vert = 0$, then stop.
Solve a minimum weight matching problem.
Update assignments – say that according to solution task $t_0$ should be assigned to machine $m_0$, then $\overline{rc}_{m_0} = rc_{m_0} - w_{t_0 m_0}$.
Find a machine where task is contributing with the lowest weight – say machine $m_0 = \arg\min \{ m: w_{t_0 m} \}$.
Free up remaining capacity so there is enough space for $t_0$ on machine $m_0$. Any tasks that were de-assigned in a process are added to pool of unassigned tasks.
Repeat until there are no unassigned tasks.

See details on github .

As we said before the most important piece needed to implement B&P is branching rules which does not destroy structure of subproblem. Let’s consider non-integral solution to RMP. Given convexity constraint it means that there exists machine $j_0$ and at least two, and for sake of example say exactly two, $0 < \lambda_{j_0} ^{k_1} < 1$ and $0 < \lambda_{j_0} ^{k_2} < 1$ such that $\lambda_{j_0} ^{k_1} + \lambda_{j_0} ^{k_2} = 1$. Since with each of $\lambda$s is connected different assignment (set of tasks), then it leads us to a conclusion that there exists task $i_0$ such that $x_{i_0 j_0} < 1$ expressed in variables from the original formulation. Now, let’s use this information to formulate branching rule:

left child node: a task $i_0$ must be assigned to a machine $j_0$.
right child node: a task $i_0$ cannot be assigned to a machine $j_0$.

We can say that branching is based on $x_{ij}$ from standard formulation. And it can be represented by:

Note that we can use the branching rule to easily to filter out initial columns for each node that do not satisfy those conditions:

$j = j_0$ and task $i_0 \in T_{j_0}$, or
$j \neq j_0$ and task $i_0 \notin T_{j}$.
$j = j_0$ and task $i_0 \in T_{j_0}$.

See on github .

Based on the same principle, subproblem’s pool of feasible solution are created - i.e. on left child node:

knapsack subproblem for machine $j_0$ – variable representing task $i_0$ is forced to be $1$.
knapsack subproblem for machine $j \neq j_0$ – variable representing task $i_0$ is forced to be $0$.

Similarly for right childe node. See on github .

Below is an outline of main loop of column generation. It is an implementation of flow diagram from above so I will not spend too much time describing it. The only part maybe worth commenting is stop_due_to_no_progress - it evaluates whether column generation did not make any progress in last $k$-iterations and it should be stop.

Now, let’s see how constructing subproblems, solving them and then adding back column(s) to RMP looks like. We have as many subproblems as machines. Once a solution is available, we check whether it has positive reduced cost. A solution to knapsack problem corresponds to column in RMP. So if the column with positive reduced cost was identified and added, then new iteration of column generation will be executed. Gurobi allows to query information about all other identified solutions, so we can utilize this feature and add all columns that have the same objective value as optimal solution, potentially adding more than one column and hoping it will positively impact solution time.

Note that each subproblem is independent so in principle they could be solved in parallel. However due to Python Global Interpreter Lock (GIL) that prevent CPU-bounded threads to run in parallel, they are solved sequentially. Additionally depending on your Gurobi license, you might not be allowed to solve all those models in parallel even if Python would allow it.

Below you can find example of one of the RMPs:

and subproblem with dual information passed:

Now that we have all building blocks prepared, then let’s turn our attention back to B&P.

In the blog post, Branch-and-Price technique for solving MIP was explained. An example of applying B&P for Generalized Assignment Problem was presented. The solution approach used Python as programming language and Gurobi as solver.

[1] Der-San Chen, Robert G. Batson, Yu Dang (2010), Applied Integer Programming - Modeling and Solution, Willey. [2] Lasdon, Leon S. (2002), Optimization Theory for Large Systems, Mineola, New York: Dover Publications. [3] Cynthia Barnhart, Ellis L. Johnson, George L. Nemhauser, Martin W. P. Savelsbergh, Pamela H. Vance, (1998) Branch-and-Price: Column Generation for Solving Huge Integer Programs. Operations Research 46(3):316-329.

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Solving an Assignment Problem

This section presents an example that shows how to solve an assignment problem using both the MIP solver and the CP-SAT solver.

In the example there are five workers (numbered 0-4) and four tasks (numbered 0-3). Note that there is one more worker than in the example in the Overview .

The costs of assigning workers to tasks are shown in the following table.

Task 0	Task 1	Task 2	Task 3
90	80	75	70
35	85	55	65
125	95	90	95
45	110	95	115
50	100	90	100

The problem is to assign each worker to at most one task, with no two workers performing the same task, while minimizing the total cost. Since there are more workers than tasks, one worker will not be assigned a task.

MIP solution

The following sections describe how to solve the problem using the MPSolver wrapper .

Import the libraries

The following code imports the required libraries.

Create the data

The following code creates the data for the problem.

The costs array corresponds to the table of costs for assigning workers to tasks, shown above.

Declare the MIP solver

The following code declares the MIP solver.

Create the variables

The following code creates binary integer variables for the problem.

Create the constraints

Create the objective function.

The following code creates the objective function for the problem.

The value of the objective function is the total cost over all variables that are assigned the value 1 by the solver.

Invoke the solver

The following code invokes the solver.

Print the solution

The following code prints the solution to the problem.

Here is the output of the program.

Complete programs

Here are the complete programs for the MIP solution.

CP SAT solution

The following sections describe how to solve the problem using the CP-SAT solver.

Declare the model

The following code declares the CP-SAT model.

The following code sets up the data for the problem.

The following code creates the constraints for the problem.

Here are the complete programs for the CP-SAT solution.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2024-08-06 UTC.

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How to fix the git error: Repository not found

I create a new project and trying to push on the new repository.

What I did is like this:

and here's the error:

I already try to remove and reapply still doesn't work.

1 Please confirm again that such a repo exists at that URL. – iBug Commented Apr 7, 2021 at 8:31
@iBug when I try to public it works but when I change to private the error appears – user13780186 Commented Apr 7, 2021 at 8:45
1 have you try to push via ssh rather than https (and set up ssh key beforehand)? – Kristian Commented Apr 7, 2021 at 9:38

2 Answers 2

If None of the protocols (ssh and https) works and such a repo exists, then

Find the answer on this post .

Solve this by simply adding username to url like below,

Before: https://gitlab.com/gitlab_user/myrepo.git

After: https:// [email protected] /gitlab_user/myrepo.git

Remember 'gitlabusername' is your gitlab user name and second gitlab_user would be project owner so the complete URL would be : https:// [email protected] /repo_owner_gitlab_username/repo_name.git

in case your repository is private, you need to use ssh that would look like [email protected] :test.dev/project1.git replacing your remote origin would help you the command to do that would look like this: git remote set-url origin [email protected] :test.dev/project1.git

in case your repository is private, you need to use ssh => I use private repos on gitlab.com over https without any problem. If there is a specific case linked to OP's question which forces him to use ssh, you should explain that in your answer. As far as I'm concerned, with the current available info, the repository just does not exist at all. – Zeitounator Commented Apr 7, 2021 at 12:20

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Computer Science > Data Structures and Algorithms

Title: pre-assignment problem for unique minimum vertex cover on bounded clique-width graphs.

Abstract: Horiyama et al. (AAAI 2024) considered the problem of generating instances with a unique minimum vertex cover under certain conditions. The Pre-assignment for Uniquification of Minimum Vertex Cover problem (shortly PAU-VC) is the problem, for given a graph $G$, to find a minimum set $S$ of vertices in $G$ such that there is a unique minimum vertex cover of $G$ containing $S$. We show that PAU-VC is fixed-parameter tractable parameterized by clique-width, which improves an exponential algorithm for trees given by Horiyama et al. Among natural graph classes with unbounded clique-width, we show that the problem can be solved in linear time on split graphs and unit interval graphs.

Comments:	19 pages, 3 figures
Subjects:	Data Structures and Algorithms (cs.DS)
Cite as:	[cs.DS]
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View a PDF of the paper titled Pre-assignment problem for unique minimum vertex cover on bounded clique-width graphs, by Shinwoo Ahn and 6 other authors View PDF HTML (experimental) Abstract: Horiyama et al. (AAAI 2024) considered the problem of generating instances with a unique minimum vertex cover under certain conditions.
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