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# Complexity 101 and the P-NP question

Anyone working with computer algorithms sometimes has to reflect on the question what the complexity of that algorithm is. Last week I tortured my poor laptop by letting it crunch away the whole night on a planning problem using A* with the $h_{max}$ heuristic, only to find that upon waking up 1) the cpu heat was quite critical and 2) that the search space exploded and the program did not terminate. In situations like this is nice to have an estimate about whether it even makes sense to wait for termination (and no it did not, the $h_{max}$ heuristic performed very poorly).

In such a scenario it is wise to do a complexity analysis of your algorithm: given a particular input size, how much space and time is required to provide the wanted output? Usually people do not go looking for a stopwatch in some dusty drawer, but are interested in the general order of complexity; it has to be “in the ballpark.” In some cases, algorithms can be plain bad: their complexity is due to bad design.

But a more fundamental issue is when a problem is simply so hard that we are not even sure an algorithm exists that can solve it efficiently. Therefore it is not only useful to discuss the complexity of algorithms, but also the hardness of problems. In this post, I want to introduce the basic concepts expressing the hardness of such problems. On top of that, these concepts will be helpful to understand one of the seven “Millenium problems”, the solving of which is rewarded with one million dollar: is the set P equal to the set NP? Read on…

## Big-O-Oh ¶

The order of complexity can be assessed in the so-called Big-O notation. It follows two simple principles:

1 - The main bottleneck for any algorithm is the most costly component of that algorithm.

2 - We do not care about the exact number of computations done, but about the relation the input size has to the required amount of space and time.

So let’s assume we have some input with size n to feed our algorithm, which requires a certain amount of time and space to process. And now assume that the input becomes twice as big. Does that mean that we also need twice as much space and time to get the algorithm to output what we want? In that case, there is a linear relation between the input size and the space or time complexity, which is denoted as O(n). If we make the input size twice as big, and it turns out that the required space or time is four times as large, then we are dealing with a quadratic relation instead, which can be expressed as O($n^2$). This illustrates bullet point 2.

To illustrate point 1, let’s build forth on the same example. If we have some algorithm that combines multiple operations, of which one is linear (O(n)) and one is quadratic (O($n^2$)), then the complexity of the algorithm is dominated by the highest-order relation, which is quadratic in this case. So in this case, we still speak of O($n^2$).

In general, we speak of polynomial time algorithms if we have, for input size n and some constant exponent c, O($n^c$). For c=1,2,3 respectively, we speak of linear, quadratic and cubic running times. Having an algorithm run in cubic time is pretty bad, but at least we can make a pretty good assessment about its feasibility in practice.

But then there are the true monstrosities of complexity: exponential time algorithms that are exponential in input size: O($c^n$), where c > 1.

Problems for which no polynomial time algorithm exists are considered to be intractable, where intractability means there exists no efficient (i.e. in polynomial time) algorithm to solve them.

## Decision problems ¶

Our main question concerned the hardness of problems. The most basic question we can ask here is whether any given problem can be solved. A second question is how hard it is to verify that a solution is correct, if someone were to come to us with a proposed solution.

Let’s say we have the following situation: we have a graph with weighted connections, and some start S and some goal G. Now our problem is that we want to find the cheapest path from S to G. We could of course go ahead and simply try to solve it. But let’s say that this unfortunately does not work out… You are scratching your head and then ask: did I not find an efficient algorithm for solving my problem, or does such an algorithm not exist?

In other words, you would want a systematic approach for deciding whether a problem is solvable. In this case you would need to ask: does there exist a path such that the path cost is at most k? Significantly, this question is answered either by yes or no, and does not yield a solution (a shortest path). This is called a decision problem. But although this type of problem does not provide us with a solution to our original search problem, it does indicate is how hard the corresponding problem is to solve!

## Classes P and NP ¶

Given these decision problems that indicate the hardness of the corresponding search or optimization problem, we can distinguish complexity classes:

### Definition of the class P ¶

P is the class of decision problems that are solvable in polynomial time, i.e. there exists an algorithm with worst case time complexity of O($i^c$) that can decide for every i $\in$ I whether D(i)=yes or D(i)=no.

### Definition of NP ¶

NP is the class of decision problems D for which yes instances (D($i_{yes}$)) are **verifiable** in polynomial time.

In other words, if someone where to come to you with a solution to your problem, how hard is it for you to verify that this solution is indeed correct? This definition of NP is slightly tricky, because it is only defined on “yes-instances”, i.e. a solution that claims to be correct.

Take a graph 3-colourability problem, taken from a lecture I followed on complexity. This means that we have some graph, and we ask whether we can colour the nodes of the graph with only three colours in such a way that none of the same coloured nodes are directly connected to each other. So the decision problem at hand here is: is there a colouring of this graph such that none of the vertices sharing an edge have the same colour?

### Example ¶

1 - Finding (“deciding”) such a colouring is hard, and cannot be guaranteed to be doable in polynomial time.

2 - Given a colouring, it is easy to verify that it is correct.

3 - Given a wrong colouring (a no-instance), it is hard to determine if the graph is not 3-colourable in general, or if the current colouring just happens to be wrong.

The third point illustrates why NP is defined on yes-instances of the decision problem, because verifying yes instances is not the same as verifying no-instances.

## Relations between P and NP ¶

The P-NP question concerns the exact relationship between the sets P and NP. In other words, if we know a problem is decidable, what does that say about its verifiability? And if a problem is verifiable, what does that imply about its decidability?

One thing is clear at least: P is a strict subset of NP. If a problem is decidable, it must also be verifiable. Consider this: if you need to verify a problem and it is in P as well, then it is always possible to throw away the solution that has to be verified and instead find it yourself, all still in polynomial time.

However, whether NP is also a strict subset of P is exactly the million-dollar question. In other words, does P=NP hold?

If there exists a proof of membership in NP (verifiable in polynomial time) but simultaneously a proof of non-membership in P (there is a super-polynomial lower bound), then we have shown that NP is not a strict subset of P and that P $\neq$ NP.

### NP-hard ¶

It has so far not been possible to find a problem that is in NP but not in P (so again, a very hard problem that we can verify but not decide in polynomial time). However, it is possible for any NP-problem to decide if it is amongst the hardest problems in NP, more specifically at least as hard as all other NP problems.

This is a meaningful insight, because if we find for a given NP-hard problem that it also belongs to P, then we have shown that all NP problems belong to P, effectively proving P = NP. Intuitively, this makes sense: if we can find a P algorithm for the hardest problem in NP, then we must also be able to do that for the easier problems in NP, given that they can be rephrased in terms of the harder decision problem.

Conversely, since we do have the intuition that P $\neq$ NP, proving membership of NP-hard is a good indication (not proof!) that the problem is verifiable but does not have a polynomial time algorithm for deciding it (P membership).

## All or nothing! ¶

To recap shortly: there are some problems that we can verify quite easily in polynomial time, but for which we do not have a polynomial time algorithm for actually finding a solution. An example was the graph 3-colouring problem. We ask ourselves: do we not have an algorithm for deciding that problem because we simply have not found it yet (so possibly P=NP), or because the problem is so hard that there simply cannot be any efficient algorithms (P$\neq$NP)?

We have also considered that proving that a P algorithm exists is doable by actually providing such an algorithm, while proving that it does not exist is extremely hard. What could be a tactic?

Consider the following figure, taken from wikipedia:

\

Just for completeness: from the illustration you can see that NP-complete is the class of problems that are both in NP and in NP-hard.

Let’s say that we suspect that there is no polynomial time algorithm for a given problem. And let’s assume I have some problem in NP of which I already know that no polynomial time algorithm exists for deciding it. If we could somehow show that this problem without efficient solution can be reduced to the problem we are considering, then we also know that the problem under consideration is not in P.

If we keep on reducing problems like this, we end up with an all or nothing issue: once we can show for one of those problems that it is not in P, then we can show it for all problems in NP that were reduced from it. Conversely, if we find that one of the NP-hard problems, to which all other NP problems can be reduced, is in P, then we have shown it for all problems in NP. To understand why, we have to carefully look at what this reduction precisely means.

### What can we conclude from a reduction? ¶

In order to preform a reduction, we need to find an algorithm that translates every instance of decision problem $D_1$ to an instance of $D_2$ such that every yes-instance still is a yes-instance, and every no-instance is still a no-instance in the other decision problem. In particular, for the above reasoning to hold we need these reductions to be possible in polynomial time.

Assume we reduce $D_1$ to $D_2$. Then every found solution to $D_2$ in polynomial time can be translated back in polynomial time to the original domain, meaning that also in that domain there must be a polynomial time algorithm.

The notation $D_1$ $\leq$ $D_2$ means that $D_1$ is reducible in polynomial time to $D_2$ (The notation is incomplete but suffices for now). We could read the smaller or equal sign also more informally as: $D_2$ is equally hard or harder than $D_1$ .

We have $D_1$ $\leq$ $D_2$. Two conclusions are possible:

1 - If $D_2$ can be decided in polynomial time, then so can $D_1$, since $D_2$ is at least as hard.

2 - If $D_1$ cannot be decided in polynomial time, then $D_2$ also cannot (since $D_2$ is at least as hard, and has the exact same solutions due to the possible reduction).

In fact, the second statement follows logically from the first: let D1 mean that $D_1$ is solvable in polynomial time and let D2 mean that $D_2$ is solvable in polynomial time. Then D2 -> D1 is equivalent to ~D1 -> ~D2 (contraposition).

If the above reduction holds, we can not conclude that if $D_1$ can be decided in polynomial time, that $D_2$ also can. The reduction only works in one direction. It shows that any solution to $D_2$ must also be a solution to $D_1$. So let’s say we find a solution for $D_1$… Then we cannot say anything about a solution to $D_2$. This is also intuitive, since $D_2$ can be way harder! A solution would of course be to show that the reduction is symmetrical.

So this is the crux: if we are able to reduce all NP problems to a set of the hardest problems in NP, i.e. NP-hard (and NP-hard problems can be reduced to each other), that means that if we find a P-time algorithm for deciding a NP-hard problem, we have one for all NP-hard problems, since there are shown to be polynomial time reductions to the NP-hard problems. Quite a mouth full!

### But how to show membership to NP-hard? ¶

It is very hard though to show membership of NP-hard because you need to show that all decision problems in NP are reducible to the problem in NP-hard. The first problem proven to be NP-hard is the CNF-Sat problem: for any logical formula in propositional logic, is there a way to make it true?

From there on the burden of proof is a bit lighter for proving problems to be NP-hard. We do not have to reduce every problem in NP to our problem of interest anymore. Now we “only” have to show that a problem is at least as hard as the CNF-Sat problem. In other words, we have to reduce from CNF-Sat to our problem of interest (not the other way around!). That guarantees that our problem of interest is indeed at least as hard as any other problem in NP. In general, once we have other NP-hard (more specifically, NP-complete) problems, the tactic is to reduce from any NP-hard problem to the problem of which we try to prove NP-hardness.

## The 1M$Question ¶ To conclude, two potential tactics for deciding this question are: 1. For P=NP. Show for a NP-hard problem that there is a polynomial-time decision possible. Since that NP-hard problem is at least as hard as any other problem in NP, we have effectively proven P=NP. 2. For P$\neq$NP. Somehow show that there is a problem for which a polynomial-time decision is not possible, but that is verifiable. In other words, that it is in NP but not in P (This is the only option, since P definitely is a strict subset of NP). Credits: This post contains my proceedings from a lecture given by Johan Kwisthout, attended at the Radboud University Nijmegen. # Setting up ssh and using tmux Ingredients: • ssh: the client command • sshd: the server component • scp and sshfs: tools to securely copy files • ssh-keygen • tmux ## Why SSH? ¶ One of the reasons I was getting more and more annoyed with Windows was that it does not have a free service for remotely connecting to another pc. There are plenty of options for remote desktop sharing using third-party software (of which TeamViewer is probably the most well-known) that can be configured such that they are sufficiently safe to use. However, depending on what the remote connection is used for, the lag of remote desktop solutions can be pretty annoying. Moreover, for some reason I fixated myself on this idea to be able to connect to any computer on university, only to find out that third-party solutions could not properly be used because I had no rights to install them. Fair enough. I quickly ended up looking into Window’s native protocol used in the “Remote Desktop” feature. Unfortunately I discovered quickly that although this feature was free to use if you want to connect to another PC, actually enabling others to connect to your own pc required a Windows Pro update, which I did not have and was not going to get just for having something that is free and secure on any Linux system. ## Installation ¶ Install the openssh protocol with your preferred package manager. In my case that is pacman: sudo pacman -S openssh  ## Configuration of the ssh server ¶ Before being able to ssh into your machine, the daemon taking care of all the ssh business must be correctly configured according to your preferences. Your configuration file is usually found in /etc/ssh/sshd_config . These are options you should consider changing from the defaults: 1. To make connections more secure and only allow known users, disallow password logins on your server. This requires validation with ssh keys to login. More on that later. Even if you enable authorization with key pairs, the server will still allow password authentication if key authentication fails, thus still exposing the server to brute force attacks. 2. Disable root login to minimize damage to the overall system in case someone acquired unwanted access to the server. 3. Enable X11 forwarding if you want to be able to start graphical applications over your ssh connection. It gives you permission to run X server. 4. If you want to login using ssh keys, make sure to give the correct path to where the keys are stored (more on actually generating them below). That sounds easy enough, nevertheless I did not got key authorization to work because I thought it was possible to refer to my home directory as I usually do with ~. It gave me a headache, but ones I located the issue I replaced ~ with the path variable %h instead. %h/.ssh/authorized_keys  1. By default port 22 is used for ssh connections. You can specify another port manually. This might decrease the amount of malicious attempts to connect to your server. ## Generating keys ¶ The server to which we want to connect now requires an ssh key for authentication, but it does not allow any connections at the moment because the .ssh/authorized_keys folder on the server side is still empty. Also make sure the .ssh folder has the right permissions: chmod 700 ~/.ssh && chmod 600 ~/.ssh/*  On the client pc with which we want to connect to the server, we first need to have a pair of keys. The following command creates a private and a public key for authentication: ssh-keygen  When generating the keyphrase you have the option to also add a passphrase as an extra security measure. By default, the public key will be stored in ~/.ssh . The public key, as the name already betrays, can be shared and exposed to the internet. But while generating the public key, a private key that should only be known to the user is also generated. This key is the key to decrypting the information stored in the public key. So information sent over the ssh connection encrypted with my public key, can only be decrypted by the ones that have access to my private key (which is hopefully only me). In order to make sure our system knows what key to use for that, add the freshly generated ssh key to your “ssh-agent”. First start the ssh-agent: eval "$(ssh-agent -s)"


Next, we add the generated key:

ssh-add ~/.ssh/id_rsa


Note that the key here ends with ‘rsa’. This is just one of the possible encryption methods, choose whichever one you like.

The only thing we still need to do now is copy our public key to the ~/.ssh/authorized_keys folder on the server, so it allows us to connect. Copy the key in any way you like. An option is to temporarily allow password authentication on your server and run:

ssh-copy-id -p port remotename@remotehostaddress


This function is pretty robust. It tries to connect with the public keys stored on your pc and adds them to the server over a password protected ssh connection, and if this fails it adds them to the remote server. If everything is set up properly, this function should not need fancy arguments but will do the work for you.

I included the port flag just in case you followed the advise to change the default port for ssh from 22 to something else. Or if you want to go for sticks and stones, just use an USB stick.

Don’t forget to disable password authentication again.

## Connecting over ssh ¶

If you want to allow ssh connections, there is one last thing you need to do: run the sshd.service daemon that allows people with the ssh tool to connect. If you want to only temporarily turn it on, run (on Arch Linux):

systemctl start sshd.service


And if you run a dedicated server to which you always want to allow ssh connections, run:

systemctl enable sshd.service


The establishment of a ssh connection should now be straightforward. Run:

ssh -p port remoteip


After running the above command, you find yourself in a single terminal logged in on the remote server. Now what? Your options are somewhat limited. If you are interested in transferring files, check out the ‘scp’ or ‘sshfs’ command (I expect to make another post about this topic later). Other than that, you can do what you would also normally do with the limitation that per ssh connection you open one single terminal. Another disadvantage is that when the ssh connection is broken, any process running in that single terminal is interrupted.

## Using Tmux ¶

Tmux is a so-called “terminal multiplexer”, which basically means that it is able to create more terminals within a single terminal. It is thus a very handy tool if you want to incorporate the ssh command into your workflow somehow. I personally don’t currently use it outside of ssh-based stuff because the i3 window manager I use is already designed precisely for easily creating new windows with terminals.

But tmux + ssh is definitely a golden combo, since you are able to overcome the limitation of only having a single terminal to run processes in. Instead of creating multiple ssh connections, you just multiplex terminals (is that even a verb?) within the same ssh session. But there is more.

The most useful advantage of using ssh in combination with tmux is probably that you can safely run a process remotely on the server, log out, and come back later to find out the process is still running. As said just now, breaking an ssh connection usually also interrupts the processes initiated through this connection. Tmux however has the possibility to “detach” a session (and it does so automatically if the ssh connection is lost), to come back to it later and “attach” to it again. So after initiating a ssh connection, install and fire up tmux.

Detach a session:

tmux detach


List all active sessions:

tmux list-sessions


Check out the id of the session you want to attach to (or its name, if you have given it while creating it) and run:

tmux attach -t sessionID


Voila!

## < tmux + ssh = OK > ¶

    \   ^__^
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This website emerged from a DIY attitude, which translates as: I have been working with raw material, without having pre-existing knowledge about proper style for either html, css, or javascript etc. I could also not be bothered to read up on theory first, since this was a project for leisure time (to avoid working on other things, basically). I started with a single page website with some html and css, and over the course of a half year I kept adding features and something resembling actual content to the page. In the meantime I was building forth on questionable and quite intractable css classes of course, often resulting in me losing oversight. The maniacal stream of git commits in the repository of this website testifies to this.

Lately I have been getting into markdown based workflows, for example as a way to replace writing lengthy LaTeX markup for university work or fancy notes. I figured that if one day I would start using this website for promoting actual content, as opposed to using it for intellectual masturbation - “look here is this webpage with my name on it” - then I would definitely want to push content written in markdown, from the comfort of my preferred working environment: the nerdy vim editor in the self-contained universe of my tiling terminal.

At the same time, I wanted to keep things basic and retain control over all aspects of my website. Self-maintainance is key for me: this whole thing has to cost less than ten bucks a year and I want to be able to edit every single line of code that contributes to my webpage. I decided to opt for a compromis: a static website generator (Hugo, in my case) that converts markdown files to html for me. Any metadata about posts (author,date,tags etc.) can also be defined in a markdown file by adding a header written in the YAML language. The transition to the new format was confusing because I again simply dove in without doing research. All I needed to know to begin was that Hugo played nice with netlify.com, where this website is currently hosted. An added advantage was that it forced me to reorganize the website. In order for the hugo server to find all required files everything needed to follow a well-defined folder structure. The main issue was figuring out how to keep my original homepage, while simultaneously setting up hugo in such a way that I could easily add new content with more rich features. Long story short: I ended up delaying university work for a day, shrunk down my original homepage to the bare minimum (a first step to cleaning up wandering css bits), and added this blog! Not too shabby.

Content
As far as the content of this blog section of my site goes: I intend to use it as a mnemonic device. Over the years I have occupied myself with studying such diverse topics, only to realize that I can barely recollect half of it. So as a rule of thumb for this blog: if I spend a considerable amount of time on one topic, I should record it in this elaborate and weirdly public note-taking system. My best guess at the moment is that those notes are likely to be related either to philosophy, artificial intelligence, or the ongoing discovery of (Arch) Linux and the design of a nice workflow. What emerges over time is hopefully a rhizomatic collection of interesting notes, where no one (including me) really has a central oversight on the contents, where there is no category table assigning everything its proper place, but where posts branch out and interlink chaos-logically. This is reflected in the only “searchable” feature of this blog: clicking on the tag of a blogpost shows you posts with the same tag.

Summary of current workflow:\

1. Markdown + vim\
2. Pushing to the website’s git repository\
3. Automatic deployment on netlify. Netlify takes the “public” directory in which the hugo server publishes the site locally, and builds it with the hugo command.

In other words, I can simply write all my content in markdown, and I never have to leave my terminal for maintaining my website.

EOF


# Incipit!

Incipit!

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EOF

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