Category: Skills and Learning

There are no rabbits pulled out of hats here – these tricks rely on mathematical principles and will never fail you, says Peter Rowlett.

LOOK, I’ve got nothing up my sleeves. There are magic tricks that work by sleight of hand, relying on the skill of the performer and a little psychology. Then there are so-called self-working magic tricks, which are guaranteed to work by mathematical principles.

For example, say I ask you to write down a four-digit number and show me. I will write a prediction but keep it secret. Write another four-digit number and show me, then I will write one and show you. Now, sum the three visible numbers and you may be surprised to find the answer matches the prediction I made when I had only seen one number!

The trick is that while the number I wrote and showed you appeared random, I was actually choosing digits that make 9 when added to the digits of your second number. So if you wrote 3295, I would write 6704. This means the two numbers written after I made my prediction sum to 9999. So, my prediction was just your original number plus 9999. This is the same as adding 10,000 and subtracting 1, so I simply wrote a 1 to the left of your number and decreased the last digit by 1. If you wrote 2864, I would write 12863 as my prediction.

Another maths trick involves a series of cards with numbers on them (pictured). Someone thinks of a number and tells you which of the cards their number appears on. Quick as a flash, you tell them their number. You haven’t memorised anything; the trick works using binary numbers.

Regular numbers can be thought of as a series of columns containing digits, with each being 10 times the previous. So the right-most digit is the ones, to its left is the tens, then the hundreds, and so on. Binary numbers also use columns, but with each being worth two times the one to its right. So 01101 means zero sixteens, one eight, one four, zero twos and one one: 8+4+1=13.

Each card in this trick represents one of the columns in a binary number, moving from right to left: card 0 is the ones column, card 1 is the twos column, etc. Numbers appear on a card if their binary equivalent has a 1 in that place, and are omitted if it has a 0 there. For instance, the number 25 is 11001 in binary, so it is on cards 0, 3 and 4.

You can work this trick by taking the cards the person’s number appears on and converting them to their binary columns. From there, you can figure out the binary number and convert it to its regular number. But here’s a simple shortcut: the binary column represented by each card is the first number on the card, so you can just add the first number that appears on the cards the person names. So, for cards 0 and 2, you would add 1 and 4 to get 5.

Many self-working tricks embed mathematical principles in card magic, memorisation tricks or mind-reading displays, making the maths harder to spot. The key is they work every time.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Peter Rowlett*

What better way is there to celebrate pi day than with a slice of mathematics? Here are 7 mathematical facts to enjoy.

There’s a mathematical trick to get out of any maze

It will soon be 14 March and that means pi day. We like to mark this annual celebration of the great mathematical constant at New Scientist by remembering some of our favourite recent stories from the world of mathematics. We have extracted a list of surprising facts from them to whet your appetite, but for the full pi day feast click through for the entire articles. These are normally only available to subscribers but to honour the world’s circumferences and diameters we have decided to make them free for a limited time.

The world’s best kitchen tile

There is a shape called “the hat” that can completely cover a surface without ever creating a repeating pattern. For decades, mathematicians had wondered whether a single tile existed that could do such a thing. Roger Penrose discovered pairs of tiles in the 1970s that could do the job but nobody could find a single tile that when laid out would have the same effect. That changed when the hat was discovered last year.

Why you’re so unique

You are one in a million. Or really, it should be 1 in a 10^{10^68.}  This number, dubbed the doppelgängion by mathematician Antonio Padilla, is so large it is hard to wrap your head around. It is 1 followed by 100 million trillion trillion trillion trillion trillion zeroes and relates to the chances of finding an exact you somewhere else in the universe. Imagining a number of that size is so difficult that the quantum physics required to calculate it seems almost easy in comparison. There are only a finite number of quantum states that can exist in a you-sized portion of space. You reach the doppelgängion by adding them all up. Padilla also wrote about four other mind-blowing numbers for New Scientist. Here they all are.

An amazing trick

There is a simple mathematical trick that will get you out of any maze: always turn right. No matter how complicated the maze, how many twists, turns and dead ends there are, the method always works. Now you know the trick, can you work out why it always leads to success?

And the next number is

There is a sequence of numbers so difficult to calculate that mathematicians have only just found the ninth in the series and it may be impossible to calculate the tenth. These numbers are called Dedekind numbers after mathematician Richard Dedekind and describe the number of possible ways a set of logical operations can be combined. When the set contains just a few elements, calculating the corresponding Dedekind number is relatively straightforward, but as the number of elements increases, the Dedekind number grows at “double exponential speed”. Number nine in the series is 42 digits long and took a month of calculation to find.

Can’t see the forest for the TREE(3)

There is a number so big that in can’t fit in the universe. TREE(3) comes from a simple mathematical game. The game involves generating a forest of trees using different combinations of seeds according to a few simple rules. If you have one type of seed, the largest forest allowed can have one tree. For two types of seed, the largest forest is three trees. But for three types of seed, well, the largest forest has TREE(3) trees, a number that is just too big for the universe.

The language of the universe

There is a system of eight-dimensional numbers called octonions that physicists are trying to use to mathematically describe the universe. The best way to understand octonions is first to consider taking the square root of -1. There is no such number that is the result of that calculation among the real numbers (which includes all the counting numbers, fractions, numbers like pi, etc.), so mathematicians add another called i. When combined with the real numbers, this gives a system called the complex numbers, which consist of a real part and an “imaginary part”, such as 3+7i. In other words, it is two-dimensional. Octonions arise by continuing to build up the system until you get to eight dimensions. It isn’t just mathematical fun and games though – there is reason to believe that octonions may be the number system we need to understand the laws of nature.

So many new solutions

Mathematicians went looking for solutions to the three-body problem and found 12,000 of them. The three-body problem is a classic astronomy problem of how three objects can form a stable orbit around each other. Such an arrangement is described by Isaac Newton’s laws of motion but actually finding permissible solutions is incredibly difficult. In 2007, mathematicians managed to find 1223 new solutions to the problem but this was greatly surpassed last year when a team found more than 12,000 more.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Timothy Revell*

One week after DeepMind revealed an algorithm for multiplying numbers more efficiently, researchers have an even better way to carry out the task.

Multiplying numbers is a common computational problem

A pair of researchers have found a more efficient way to multiply grids of numbers, beating a record set just a week ago by the artificial intelligence firm DeepMind.

The company revealed on 5 October that its AI software had beaten a record that had stood for more than 50 years for the matrix multiplication problem – a common operation in all sorts of software where grids of numbers are multiplied by each other. DeepMind’s paper revealed a new method for multiplying two five-by-five matrices in just 96 multiplications, two fewer than the previous record.

Jakob Moosbauer and Manuel Kauers at Johannes Kepler University Linz in Austria were already working on a new approach to the problem prior to that announcement.

Their approach involves running potential multiplication algorithms through a process where multiple steps in the algorithm are tested to see if they can be combined.

“What we do is, we take an existing algorithm and apply a sequence of transformations that at some point can lead to an improvement. Our technique works for any known algorithm, and if we are lucky, then [the results] need one multiplication less than before,” says Moosbauer.

After DeepMind published its breakthrough, Moosbauer and Kauers used their approach to improve on DeepMind’s method, slicing off another step to set a new record of 95 multiplications. They have published the proof in a pre-print paper, but haven’t yet released details of the approach they used to find improvements on previous methods.

“We wanted to publish now to be the first one out there, because if we can find it in such a short amount of time, there’s quite some risk that we get outdone by someone else again,” says Moosbauer.

The latest paper is entirely focused on five-by-five matrix multiplication, but the method is expected to bring results for other sizes. The researchers say that they will publish details of their technique soon.

Moosbauer says that DeepMind’s approach brought fresh impetus to an area of mathematics that hadn’t been receiving much attention. He hopes that other teams are also now working in a similar vein.

Matrix multiplication is a fundamental computing task used in virtually all software to some extent, but particularly in graphics, AI and scientific simulations. Even a small improvement in the efficiency of these algorithms could bring large performance gains, or significant energy savings.

DeepMind claimed to have seen a boost in computation speed of between 10 and 20 per cent on certain hardware such as an Nvidia V100 graphics processing unit and a Google tensor processing unit v2. But it said that there was no guarantee that similar gains would be seen on everyday tasks on common hardware. Moosbauer says he is sceptical about gains in common software, but that for large and specialised research tasks there could be an improvement.

DeepMind declined a request for an interview about the latest paper, but its researcher Alhussein Fawzi said in a statement: “We’ve been overwhelmed by the incredible reaction to the paper. Our hope was that this work would open up the field of algorithmic discovery to new ideas and approaches. It’s fantastic to see others exploring ideas in this space as well as building on our work so quickly.”

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Matthew Sparkes*

Can three cubed numbers be added up to give 42? Until now, we didn’t know

It might not tell us the meaning of life, the universe, and everything, but mathematicians have cracked an elusive problem involving the number 42.

Since the 1950s, mathematicians have been puzzling over whether any integer – or whole number – can be represented as the sum of three cubed numbers.

Put another way: are there integers k, x, y and z such that k = x³ + y³ + z³ for each possible value of k?

Andrew Booker at Bristol University, UK, and Andrew Sutherland at the Massachusetts Institute of Technology, US, have solved the problem for the number 42, the only number less than 100 for which a solution had not been found.

Some numbers have simple solutions. The number 3, for example, can be expressed as 1³ + 1³ + 1³ and 4³ + 4³ + (-5) ³ . But solving the problem for other numbers requires vast strings of digits and, correspondingly, computing power.

The solution for 42, which Booker and Sutherland found using an algorithm, is:

42 = (-80538738812075974)³ + 80435758145817515³ + 12602123297335631³

They worked with software firm Charity Engine to run the program across more than 400,000 volunteers’ idle computers, using computing power that would otherwise be wasted. The amount of processing time is equivalent to a single computer processor running continuously for more than 50 years, says Sutherland.

Earlier this year, Booker found a sum of cubes for the number 33, which was previously the lowest unsolved example.

We know for certain that some numbers, such as 4, 5 and 13, can’t be expressed as the sum of three cubes.

The problem is still unsolved for 10 numbers less than 1000, the smallest of which is 114.

The team will next search for another solution to the number 3.

“It’s possible we’ll find it in the next few months; it’s possible it won’t be for another hundred years,” says Booker.

People interested in aiding the search can can volunteer computing power through Charity Engine, says Sutherland.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Donna Lu*