Ciro Santilli @cirosantilli 37

Peter Cordes mentions that some architectures like MIPS leave paging almost completely in the hands of software: a TLB miss runs an OS-supplied function to walk the page tables, and insert the new mapping into the TLB. In such architectures, the OS can use whatever data structure it wants.

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Quote by Bill Gates Updated 2025-07-16

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SymPy Updated 2025-07-16

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This is the dream cheating software every student should know about.

It also has serious applications obviously. www.sympy.org/scipy-2017-codegen-tutorial/ mentions code generation capabilities, which sounds super cool!

The code in this section was tested on sympy==1.8 and Python 3.9.5.

Let's start with some basics. fractions:

from sympy import *
sympify(2)/3 + sympify(1)/2

outputs:

7/6

Note that this is an exact value, it does not get converted to floating-point numbers where precision could be lost!

We can also do everything with symbols:

from sympy import *
x, y = symbols('x y')
expr = x/3 + y/2
print(expr)

outputs:

x/3 + y/2

We can now evaluate that expression object at any time:

expr.subs({x: 1, y: 2})

outputs:

4/3

How about a square root?

x = sqrt(2)
print(x)

outputs:

sqrt(2)

so we understand that the value was kept without simplification. And of course:

sqrt(2)**2

outputs 2. Also:

sqrt(-1)

outputs:

I is the imaginary unit. We can use that symbol directly as well, e.g.:

I*I

gives:

-1

Let's do some trigonometry:

cos(pi)

gives:

-1

and:

cos(pi/4)

gives:

sqrt(2)/2

The exponential also works:

exp(I*pi)

gives;

-1

Now for some calculus. To find the derivative of the natural logarithm:

from sympy import *
x = symbols('x')
print(diff(ln(x), x))

outputs:

1/x

Just read that. One over x. Beauty. And now for some integration:

print(integrate(1/x, x))

outputs:

log(x)

OK.

Let's do some more. Let's solve a simple differential equation:

y''(t) - 2y'(t) + y(t) = sin(t)

Doing:

from sympy import *
x = symbols('x')
f, g = symbols('f g', cls=Function)
diffeq = Eq(f(x).diff(x, x) - 2*f(x).diff(x) + f(x), sin(x)**4)
print(dsolve(diffeq, f(x)))

outputs:

Eq(f(x), (C1 + C2*x)*exp(x) + cos(x)/2)

which means:

f (x) = C_{1} + C_{2} x e^{x} + cos (x) /2

(1)

To be fair though, it can't do anything crazy, it likely just goes over known patterns that it has solvers for, e.g. if we change it to:

diffeq = Eq(f(x).diff(x, x)**2 + f(x), 0)

it just blows up:

NotImplementedError: solve: Cannot solve f(x) + Derivative(f(x), (x, 2))**2

Sad.

Let's try some polynomial equations:

from sympy import *
x, a, b, c = symbols('x a b c d e f')
eq = Eq(a*x**2 + b*x + c, 0)
sol = solveset(eq, x)
print(sol)

which outputs:

FiniteSet(-b/(2*a) - sqrt(-4*a*c + b**2)/(2*a), -b/(2*a) + sqrt(-4*a*c + b**2)/(2*a))

which is a not amazingly nice version of the quadratic formula. Let's evaluate with some specific constants after the fact:

sol.subs({a: 1, b: 2, c: 3})

which outputs

FiniteSet(-1 + sqrt(2)*I, -1 - sqrt(2)*I)

Let's see if it handles the quartic equation:

x, a, b, c, d, e, f = symbols('x a b c d e f')
eq = Eq(e*x**4 + d*x**3 + c*x**2 + b*x + a, 0)
solveset(eq, x)

Something comes out. It takes up the entire terminal. Naughty. And now let's try to mess with it:

x, a, b, c, d, e, f = symbols('x a b c d e f')
eq = Eq(f*x**5 + e*x**4 + d*x**3 + c*x**2 + b*x + a, 0)
solveset(eq, x)

and this time it spits out something more magic:

ConditionSet(x, Eq(a + b*x + c*x**2 + d*x**3 + e*x**4 + f*x**5, 0), Complexes)

Oh well.

Let's try some linear algebra.

m = Matrix([[1, 2], [3, 4]])

Let's invert it:

m**-1

outputs:

Matrix([
[ -2,    1],
[3/2, -1/2]])

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Tangible and intangible assets Updated 2025-07-16

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Uncomputable function Updated 2025-07-16

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The prototypical example is the Busy beaver function, which is the easiest example to reach from the halting problem.

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