# Cyclic Tower of Hanoi – analysis and implementation

The Tower of Hanoi problem consists of moving a size-ordered stack of n discs from one tower to another tower, out of three towers {A, B, C}, one disc at a time without putting a larger disc on top of a smaller one. The cyclic version of this problem adds the further constraint that a disc can only move through the towers in cycles, eg B -> C -> A.

Regardless of the details of the different versions, the problem has one useful invariant: the nth disc can only ever move to an empty tower because there are no discs larger than it. So wherever the nth disc moves, the top n-1 discs can move to any tower regardless of where the nth disc is without breaking the size-order rule. So this implies a recursive process in which we get the nth disc to it’s final position, but also apply that process to the top n-1 discs BEFORE applying it to the nth disc. But due to the cyclic constraint, we’ll need two versions of each process – one that moves m discs to an adjacent tower, and one that moves m discs to the post-adjacent tower. For moving one disc, there’s no difference than with applying the same function twice, but for n > 1 discs, moving to the post-adjacent tower can be done more efficiently than moving all the discs to the adjacent tower twice.

So first, we’ll represent our three towers using:

```constexpr int A = 0, B = 1, C = 2, TOWERS = 3;
```

You could use enum classes, but ints are easier for now. Since we are doing this in cyclic order, we may as well define a function to get the next tower:

```constexpr auto next_tower(auto _t)
{
return (_t + 1) % TOWERS;
}
```

constexpr ensures compilation time evaluation to speed things up a bit but also is a requirement if we want to use template metaprogramming. The modulo wraps the tower addition around back to A(0). Next is to represent our towers and discs. The discs are represented by the natural numbers from 1 to n, thereby also representing their relative sizes and thus ordering constraint. An empty position is represented with a 0-disc. While we’re at it, we’ll also print out the towers for demonstration purposes.

```int tower[3][12] = {{1,2,3,4,5,6,7,8,9,10,11,12}, {0}, {0}};
constexpr int DISCS = std::extent<decltype(tower), 1>::value;

const auto disc_width = std::to_string(DISCS).length();

/*
The towers are printed horizontally to avoid having to align columns.
--------------------------------2--7-11-14-15-16|A
--------------------------------1--3--5--8-10-13|B
--------------------------------------4--6--9-12|C
*/
void print_tower()
{
for(auto t = 0; t < TOWERS; ++t)
{
for(auto d = 0; d < DISCS; ++d)
{
if(tower[t][d]) std::cout << "-" << std::setw(disc_width) << std::setfill('-') << tower[t][d];
}
std::cout << "|" << static_cast<char>('A' + t);
std::cout << std::endl;
}
std::cout << std::endl;
}
```

For verification purposes, you can use the algorithm std::is_sorted(…) on each tower to assert that the towers are do not violate size-ordering. For the disc transfer functions, I use template metaprogramming to illustrate compile-time generation of the move sequence. We must define the template definition before we define the specializations. The definition of the general function is to move n discs from one tower to another tower (doesn’t matter which). We’ll also include a conditional parameter for choosing moving to either an adjacent or post-adjacent tower.

```auto rfind_zero(auto _first, auto _last)
{
typedef std::reverse_iterator<decltype(_first)> riter;
return std::find(riter(_last), riter(_first), 0).base();
}

template<int Discs, int Src, int Dst, bool direct = next_tower(Src) == Dst>
struct transfer;

template<int Src, int Dst>
struct transfer<1, Src, Dst, true>
{
static inline void disc()
{
auto top_src = rfind_zero(tower[Src], tower[Src] + DISCS);
auto top_dst = rfind_zero(tower[Dst], tower[Dst] + DISCS) - 1;
std::iter_swap(top_src, top_dst);
print_tower();
}
};
```

The templated structure definition does not actually define a complete structure. This is okay as we should expect that we will cover all cases. Leaving it undefined like this means that if we made a mistake in our implementation, the compiler will tell us with a compiler error saying that there is no definition. The direct boolean is a default parameter calculated from the source and destination tower arguments. This means that in the specialization, the compiler will calculate it for us and we would not need to specify whether it is direct or not. This shows how the power of the strong static typing in C++ allows us to check algorithm validity without having to run it.

To move a disc from one tower to another, we find the top disc of the source tower, remove it, and place it on top of the destination tower. The top disc of the first tower is the first non-zero element in our source tower array. The top of the destination tower which we place our disc is the element before the first non-zero element in the destination tower array. I use a reverse linear search (std::find with reverse iterators) because the array is small and the first non-zero disc will regularly be closer to the bottom than the top (beginning of the array), but using binary search (std::upper_bound) will work equally well. I use std::iter_swap to do the actual transfer of the disc from one tower to the next since, by definition, it will always swap with a 0-disc, so it will look like we actually moved something.

Next we’ll need to specialize for moving one disc to a post-adjacent tower – the indirect specialization:

```template<int Src, int Dst>
struct transfer<1, Src, Dst, false>
{
static inline void disc()
{
transfer<1, Src, next_tower(Src)>::disc();
transfer<1, next_tower(Src), Dst>::disc();
}
};
```

This is the only case where moving to a post-adjacent tower is equal to moving to the adjacent tower twice. We can cheat a bit here where we can simulate two transfers by calling the direct specialization with the Src and Dst towers, as long as any metadata function (such as move counts, or move listing) is also simulated in the output.

These two specializations are the base cases and as you can see they will never fall down further to the undefined structure definition above. There is also another opportunity for optimization by keeping track of the top disc for each tower so that we can avoid the search. Then we’ll need to generalize these two functions for cases with more than one disc.

```template<int Discs, int Src, int Dst>
struct transfer<Discs, Src, Dst, true>
{
static inline void disc()
{
transfer<Discs-1, Src, next_tower(Dst)>::disc();
transfer<1, Src, Dst>::disc();
transfer<Discs-1, next_tower(Dst), Dst>::disc();
}
};
```

To move an n-tower of discs to an adjacent tower, if we remember our invariant, we first have to make room so that we can move the nth disc into the final position. So first, we’ll need to move n-1 discs from the source tower to the tower after next. Then we move the nth disc to the adjacent tower. Now the destination tower is the post-adjacent tower to where our n-1 discs are temporarily stacked, so we’ll do another indirect transfer as before. Now you may wonder why we can call the indirect transfer for n > 1 discs before we specialize it. The answer is that we’ve only defined the templates, but we haven’t used them yet – we haven’t yet referred to a class where we’ve nailed down all of the template parameters, so they still only exist “in the ether”. But we have to specialize the remaining function before we make the actual call:

```template<int Discs, int Src, int Dst>
struct transfer<Discs, Src, Dst, false>
{
static inline void disc()
{
transfer<Discs-1, Src, Dst>::disc();
transfer<1, Src, next_tower(Src)>::disc();
transfer<Discs-1, Dst, Src>::disc();
transfer<1, next_tower(Src), Dst>::disc();
transfer<Discs-1, Src, Dst>::disc();
}
};
```

To move an n-tower of discs to the post-adjacent tower, we must do a little bit more. In essence, we’ll have to make space twice to allow the nth disc to move to its final position. So we make space the first time by transferring the top n-1 discs to the post-adjacent tower. Then the nth disc moves to the adjacent tower. Then we move n-1 discs to its adjacent tower. The nth disc is moved again to its adjacent tower and has reached its destination tower. So all that’s left is the move the n-1 discs to the destination tower, which is an indirect transfer. Finally, we define our main function:

```int main()
{
print_tower();
transfer<DISCS, A, B>()();

return 0;
}
```

You can add a counter and check that the number of single disc transfers equals the numbers given by the following table:

http://www.wolframalpha.com/input/?i=%28%281+%2B+sqrt%283%29%29%5E%28n%2B1%29+-+%281+-+sqrt%283%29%29%5E%28n%2B1%29%29%2F%282+*+sqrt%283%29%29+-+1%2C+n+%3D+1%2C+16

# Radix sort – another language comparison exercise

I was reading the Wikipedia page for Radix sort[1] and I noticed a pretty terrible example for C++, so I set about writing my own, which I’ve included in the wiki as a modernized C++14 version.

```#include <iostream>
#include <algorithm>
#include <array>
#include <vector>
#include <random>

int main()
{
using INT = std::mt19937_64::result_type;
std::array<INT, 200000> randoms;
std::generate(randoms.begin(), randoms.end(), [engine = std::mt19937_64{}]() mutable {return engine();});
std::array<std::vector<INT>, 256> buckets;

for (auto r : randoms) std::cout << r << std::endl;
std::cout << std::endl;

for (int i = 0; i < sizeof(INT); ++i)
{
for (auto r : randoms) buckets[(r >> (i << 3)) & 0xFF].push_back(r);

auto j = randoms.begin();
for (auto& bucket : buckets)
{
for (auto b : bucket) *(j++) = b;
bucket.clear();
}
}

for (auto r : randoms) std::cout << r << std::endl;

return 0;
}
```

# More on brevity and clarity

Continuing on from the previous post, there was another side challenge to implement the POSIX utility wc. Someone claimed C++ makes things unnecessarily hard and the challenge was supposed to prove it. Well, it was simple and I threw in a simple (incomplete) SLOC counter as well. The challenger couldn’t argue that C++ made it hard to implement wc, and so decided to nitpick on small things that do not even relate to the challenge at hand, mostly around coding style preferences that have nothing to do with the ease of implementing the core functionality of wc.

I’m by no means the best C++ coder in terms of complexity or style. Judge for yourself whether or not this was impossible to do cleanly in C++:

```#include <iostream>
#include <fstream>
#include <sstream>
#include <algorithm>

enum class char_opts
{
BYTES,
CHARS,
NUM_OPTS
};

void count(std::istream& _in, unsigned& sloc_count, unsigned &line_count, unsigned &word_count, unsigned &char_count, unsigned &byte_count, unsigned &max_line_length, unsigned &find_count, const std::string &str)
{
std::string line;
std::getline(_in,  line);
bool in_block_comment = false;
for (unsigned lc = 0; _in; std::getline(_in, line), ++lc)
{
byte_count += line.length();
char_count += line.length();
if (!_in.eof())
{
++line_count;
++byte_count;
++char_count;
}
max_line_length = std::max<unsigned>(max_line_length, line.length());
if (!str.empty()) for (auto s = line.find(str); s != std::string::npos; s = line.find(str, s+1), ++find_count);

std::istringstream line_str{line};
std::skipws(line_str);
std::string word;
line_str >> word;
for (; line_str; line_str >> word) ++word_count;

auto trimmed = line;
trimmed.erase(0, trimmed.find_first_not_of(" t"));
auto trailing = trimmed.find_last_not_of(" t");
if (trailing != std::string::npos) trimmed.erase(trailing);
if (!trimmed.empty() && trimmed != "{" && trimmed != "}" && trimmed.find("//") != 0) ++sloc_count;
}
}

int main(int _c, char** _v)
{
char_opts copts = char_opts::NUM_OPTS;
bool sloc = false;
bool lines = false;
bool words = false;
bool line_length = false;
std::string str;

bool opts_supplied = false;

auto args = _v + 1;
const auto end = _v + _c;
for (; args < end; ++args)
{
std::string arg{*args};
if (arg == "-" || arg[0] != '-') break;

if (arg == "-c" || arg == "-bytes") copts = char_opts::BYTES;
else if (arg == "-m" || arg == "-chars") copts = char_opts::CHARS;
else if (arg == "-L" || arg == "-max-line-length") line_length = true;
else if (arg == "-sloc") sloc = true;
else if (arg == "-l" || arg == "-lines") lines = true;
else if (arg == "-w" || arg == "-words") words = true;
else if (arg == "-o") str = *++args;
else
{
std::cerr << "Invalid argument '" <<  arg << ''' << std::endl;
return -1;
}

opts_supplied = true;
}

if (!opts_supplied)
{
copts = char_opts::BYTES;
lines = true;
words = true;
line_length = true;
}

unsigned file_count = 0;
unsigned total_sloc_count = 0;
unsigned total_line_count = 0;
unsigned total_word_count = 0;
unsigned total_char_count = 0;
unsigned total_byte_count = 0;
unsigned total_max_line_length = 0;
unsigned total_find_count = 0;
for (bool no_file = args == end; no_file || args < end; ++args, ++file_count, no_file = false)
{
std::string filename{no_file ? "" : *args};
unsigned sloc_count = 0;
unsigned line_count = 0;
unsigned word_count = 0;
unsigned char_count = 0;
unsigned byte_count = 0;
unsigned max_line_length = 0;
unsigned find_count = 0;
if (no_file || filename == "-") std::cin.clear();
count(no_file || filename == "-" ? std::cin : std::move(std::ifstream{filename}), sloc_count, line_count, word_count, char_count, byte_count, max_line_length, find_count, str);
std::cout << (sloc ? std::to_string(sloc_count) + " " : "")
<< (lines ? std::to_string(line_count) + " " : "")
<< (words ? std::to_string(word_count) + " " : "")
<< (copts != char_opts::NUM_OPTS ? std::to_string(copts == char_opts::BYTES ? byte_count : char_count) + " " : "")
<< (line_length ? std::to_string(max_line_length) + " " : "")
<< (!str.empty() ? std::to_string(find_count) + " " : "")
<< filename <<  std::endl;

total_sloc_count += sloc_count;
total_line_count += line_count;
total_word_count += word_count;
total_char_count += char_count;
total_byte_count += byte_count;
total_max_line_length = std::max(total_max_line_length, max_line_length);
}

if (file_count > 1) std::cout << (sloc ? std::to_string(total_sloc_count) + " " : "")
<< (lines ? std::to_string(total_line_count) + " " : "")
<< (words ? std::to_string(total_word_count) + " " : "")
<< (copts != char_opts::NUM_OPTS ? std::to_string(copts == char_opts::BYTES ? total_byte_count : total_char_count) + " " : "")
<< (line_length ? std::to_string(total_max_line_length) + " " : "")
<< (!str.empty() ? std::to_string(total_find_count) + " " : "")
<< "total" <<  std::endl;

return 0;
}
```

# Interesting exercise in brevity and clarity

Recently had a discussion and challenge in comparing two languages, C++ and Python. I think modern C++ is holds up really well to so-called scripting languages to do quick and dirty utility programs. This is a reasonably short implementation of a prime number finder:

```#include <cstdio>
inline bool prime(const auto _candidate, const auto *_first, const auto *_last) {
for (auto p = _first; p != _last && *p * *p <= _candidate; ++p)
if (_candidate % *p == 0)
return false;
return true;
}
int main(int _c, char** _v) {
const unsigned num_primes = 10000;
static unsigned primes[num_primes] = {2, 3};
for (unsigned i = 2; i < num_primes; ++i)
for (primes[i] = primes[i-1] + 2; !prime(primes[i], primes + 1, primes + i); primes[i] += 2);
printf("The %uth prime is: %u.n", num_primes, primes[num_primes - 1]);
return 0;
}
```

Several things. Mostly I’ve learned to re-embrace the spirit of C/C++ for brevity, such as single statement if and for blocks. But you really have to think about readability when you code in that style. The brief C style is only bad if it’s done without consideration about code aesthetics. The brief style shouldn’t be about reducing line count, but about increasing readability. It is a bit counter-intuitive coming from a university education that told you to put every if block in braces over multiple lines.

When coded in such a manner, modern C++ can approach the ease of writing that languages like Python enjoy.

# Template meta-programming rule of thumb

“Use template meta-programming to express design, not to express computation.”

Various explanations of template meta-programming uses examples like a compile-time Fibonnacci sequence. What those tutorials should be focusing on is how to use template meta-programming to hide incidental requirements of interfaces.

The ultimate goal of template meta-programming is to enable code like this:

```int main()
{
do_what_i_expect(/* args */);
return 0;
}
```

# Making templates easier with named-arguments

Say you have a template that requires a large number of arguments. ie, more than three. This is not a very clear or concise interface and not self-documenting. Any thing with a large number of arguments suffers from the same problem. You can give default arguments but if you just want to provide one argument that happens to be after one or more other default arguments, you have to provide those as well and you have to know the defaults if you want to keep the default behaviour bar the one you want to modify. My solution to provide named template arguments is this:

```template<typename NumType,
NumType TLow = std::numeric_limits<NumType>::lowest(),
NumType TMin = std::numeric_limits<NumType>::min(),
NumType TMax = std::numeric_limits<NumType>::max(),
NumType TDef = 0,
NumType TInv = -1,
typename Specials = TestList<NumType>,
typename Excludes = TestList<NumType> >
struct NumWrapper
{
static constexpr NumType low = TLow;
static constexpr NumType min = TMin;
static constexpr NumType max = TMax;
static constexpr NumType def = TDef;
static constexpr NumType inv = TInv;
typedef Specials inc_type;
typedef Excludes exc_type;

template<NumType Special>
struct Min
{
typedef NumWrapper<NumType, low, Special, max, def, inv, Specials, Excludes> type;
};

template<NumType Special>
struct Max
{
typedef NumWrapper<NumType, low, min, Special, def, inv, Specials, Excludes> type;
};

template<NumType Special>
struct Low
{
typedef NumWrapper<NumType, Special, min, max, def, inv, Specials, Excludes> type;
};

template<NumType Special>
struct Def
{
typedef NumWrapper<NumType, low, min, max, Special, inv, Specials, Excludes> type;
};

template<NumType Special>
struct Inv
{
typedef NumWrapper<NumType, low, min, max, def, Special, Specials, Excludes> type;
};

template<NumType... List>
struct Inc
{
typedef NumWrapper<NumType, low, min, max, def, inv, TestList<NumType, List...>, exc_type> type;
};

template<NumType... List>
struct Exc
{
typedef NumWrapper<NumType, low, min, max, def, inv, inc_type, TestList<NumType, List...> > type;
};

NumType val = def;
NumWrapper() = default;
NumWrapper(NumType _v) : val(_v) {}

operator NumType ()
{
return val;
}
};
```

This class is something I needed to quickly create data ranges really easily in order to generate values for testing. I may want to provide a different minimum that is different from the underlying type but use the std::numeric_limits for the other values, or I may want to provide extra values that have a special meaning within the context of its use.

The named argument effect is achieved by declaring nested classes in the NumWrapper classes that have an internal typedef that creates a new NumWrapper type from the enclosing template instantiation. The internal typedef only instantiates on the template argument they “name”, and use the rest of the values from the enclosing template instantiation. The use of default template arguments in the main definition, and the inheritance of those arguments as you continue the typedef chain means the user does not then have to provide those values if they don’t want to.

Take special note that, as the library developer, you will of course need to know the order of the template arguments. You just have to make it so that the user of your library does not have to know the order.

Declaring a new integer type becomes as simple as this:

```typedef NumWrapper<short>::Max<9999>::type::Def<1>::type::Min<-10>::type::Inc<2,3,5,7,11,13,17,19>::type MyIntegralType;
```

It also makes it easy to figure out what the expected range and values of this integral type should be. You can even automate its specialization for std::numeric_limits:

```namespace std
{
template<typename NumType, NumType TLow, NumType TMin, NumType TMax, NumType TDef, NumType TInv, typename Specials, typename Excludes>
struct numeric_limits<NumWrapper<NumType, TLow, TMin, TMax, TDef, TInv, Specials, Excludes> > : numeric_limits<NumType>
{
static constexpr bool is_specialized = true;
static constexpr NumType min()
{
return NumWrapper<NumType, TLow, TMin, TMax, TDef, TInv, Specials, Excludes>::min;
}

static constexpr NumType max()
{
return NumWrapper<NumType, TLow, TMin, TMax, TDef, TInv, Specials, Excludes>::max;
}

static constexpr NumType lowest()
{
return NumWrapper<NumType, TLow, TMin, TMax, TDef, TInv, Specials, Excludes>::low;
}
};

template<typename NumType, NumType TLow, NumType TMin, NumType TMax, NumType TDef, NumType TInv, typename Specials, typename Excludes>
struct is_arithmetic<NumWrapper<NumType, TLow, TMin, TMax, TDef, TInv, Specials, Excludes>> : is_arithmetic<NumType> {};
}
```

This way, the user will never have to specialize std::numeric_limits ever again.

One final note, you can use preprocessor macros to make this even more easier to write:

```#define NUMTYPE(type) NumWrapper<type>
#define TLOW(low) ::Low<low>::type
#define TMIN(min) ::Min<min>::type
#define TMAX(max) ::Max<max>::type
#define TDEF(def) ::Def<def>::type
#define TINV(inv) ::Inv<inv>::type
#define TINC(...) ::Inc<__VA_ARGS__>::type
#define TEXC(...) ::Exc<__VA_ARGS__>::type

typedef NUMTYPE(short)TMAX(9999)TDEF(1)TMIN(-10)TINC(2,3,5,7,11,13,17,19) MyIntegralType;
```

# Responses to the Invalid value concept, and responses to those

You could argue that an end iterator is the archetypal example of an “invalid value”. And end iterator does not represent any valid part of a vector. It’s one-past-the-end, which serves as a marker. And not even that, in set and map, where the one-past-the-end end iterator doesn’t even point to the physical end of the container’s range.

Or what if you’re writing a test generation library?1 You need to test invalid values, and you need a way to signal “I want you to generate an invalid value of a class to test this function that uses it”. For example, a function may only take a certain integer range. Any value outside of that range is invalid, and you should test for it.

If you should never create an invalid value, you may as well say you should never test how invalid values are handled.

I did consider calling it a null value, but a null value can be a valid representation of a concept. For a contrived example, what if you’re writing a simple wrapper around BSD sockets? If a socket cannot be created, you get a -1. That is an invalid value. If the divine decree is that a constructor should never create an invalid value, then such a socket wrapper class can never be a legal construct.

You could argue that you should throw an exception if an invalid value is about to be created, but exceptions aren’t always the desired behaviour.

1. Which is how I found a potential need for it.﻿

# The Internals Pattern, or emulating Ada’s discriminated variant records

In C, unions are not typesafe and requires that the programmer be completely responsible with how the unions are used. Ada’s discriminated variant records are a typesafe way of doing unions and using the Internals pattern1 we can get the same functionality in C++. This is all the code that’s needed:

```template<typename...>
struct Internals
{
// Empty struct for the default case
};
```

Due to C++’s template instantiation rules, you can have different members of a class for any specialization of the template. In essence:

```template<>
struct Internals<int>
{
int int_data;  // Note that there is no requirement that the members are the same
};

template<>
struct Internals<std::string>
{
std::string string_data;
};

template<>
struct Internals<int, int>
{
std::tuple<int, int> int_tuple_data;
};
```

All of these structs are completely different types, even though they all come from the Internals templated type. They bear no relation to each other in the way inheritance would have done, and this is the main benefit. You can have compile-time polymorphism that is checked before you even run the code. This is now possible:

```template<typename T, typename U, typename... Vs>
struct Scaffold
{
Internals<Scaffold> internals;
void print()
{
std::cout << "Default dummy data" << std::endl;
}
};

template<>
struct Internals<Scaffold<int, float, char, double>>
{
std::string data{"Specialized dummy data"};
};

template<>
void Scaffold<int, float, char, double>::print()
{
std::cout << this->internals.data << std::endl;
}

int main()
{
Scaffold<long, long>{}.print();
Scaffold<int, float, char, double>{}.print();

return 0;
}
```

The output of this program is:

Default dummy data
Specialized dummy data

This is a useful behaviour when you want something akin to aspects, or crosscutting concerns. Clients of your framework can specialize the internals without having to derive from any framework class, decoupling the framework scaffolding from the client.

1. I have not found this pattern discussed anywhere and so do not know the correct name even if there is one

# A general algorithm for modern C++11 design

C++11’s main domain is systems programming. A system that does anything interesting is made up of components and its complexity emerges from the interaction of those components. Software engineering is about managing complexity, and C++11 helps you manage that complexity by offering you multiple programming styles1. As programmers, we choose which style best reduces the complexity of expressing the system’s constraints. One size doesn’t fit all.

This is my ad-hoc process whenever I’m in my design mode:

1. Imagine a new programming language that does what you need. Don’t limit the syntax to C++11 – consider constructs from other languages or that don’t even exist.
2. Imagine short sequences of actions written in that language that achieves the main goals of the system.
3. Break down those actions into primitives – verbs and nouns that can be composed in any manner of sentences.
4. Read up on C++11 and keep an eye out for language features that look like those primitives – eg, operator overloading, lambdas, return type deduction, variadics.
5. Consider any similarities between primitives and work out if the differences between the similar primitives can be figured out during compile time by the compiler, or can only be figured out at runtime.
6. If it can be figured out during compile time, consider using templates.
7. If using templates, consider using preprocessor macros that improve readability and usability of using templates.

1. Not paradigm. I view it as a stylistic choice. There is some aesthetic aspect to looking at code, but it’s a stretch to call syntactic sugar a paradigm.

# While I’m at it…

I guess my first piece of advice to coding C++11 is to always read the Standard Library documentation. Especially the Algorithms of the STL. Many complex algorithms, especially those that require loops, can be reduced to just a few lines of non-loop code. But an even more basic benefit is to never rely on remembering what anything is supposed to do. Just browsing the algorithms and rereading the specifications of those algorithms can often lead to ingenious ways to combine algorithms.