Lets say we have an expression like
2f(n) = O(2g(n))
I don't understand the expression. I know that what f(n) = O(n) is. It basically means that the left side is asymptotically bounded above at O(n).
O being the Big O notation.
Basically it means 2g(n) is an asymptotic upper bound for 2f(n).
Now one can think this is the same as f(n) ∈ O(g(n)), but this is only correct in one direction.
2f(n) ∈ O(2g(n)) ⇒ f(n) ∈ O(g(n))
But the other way around is not correct.
E.g.:
f(n) = 2n, g(n) = n so
2n ∈ O(n) holds, but 22n = 4n ∉ O(2n).
Related
This question is better explained with an example. Suppose I want to prove the following lemma:
lemma int_inv: "(n::int) - (n::int) = (0::int)"
How I'd informally prove this is something along these lines:
Lemma: n - n = 0, for any integer n and 0 = abs_int(0,0).
Proof:
Let abs_int(a,b) = n for some fixed natural numbers a and b.
--- some complex and mind blowing argument here ---
That means it suffices to prove that a+b+0 = a+b+0, which is true by reflexivity.
QED.
However, I'm having trouble with the first step "Let abs_int(a,b) = n". The let statement doesn't seem to be made for this, as it only allows one term on the left side, so I'm lost at how I could introduce the variables a and b in an arbitrary representation for n.
How may I introduce a fixed reprensentation for a quotient type so I may use the variables in it?
Note: I know the statement above can be proved by auto, and the problem may be sidestepped by rewriting the lemma as "lemma int_inv: "Abs_integ(a,b) - Abs_integ(a,b) = (0::int)". However, I'm looking specifically for a way to prove by introducing an arbitrary representation in the proof.
You can introduce a concrete representation with the theorem int.abs_induct. However, you almost never want to do that manually.
The general method of proving statements about quotients is to first state an equivalent theorem about the underlying relation, and then use the transfer tool. It would've helped if your example wasn't automatically discharged by automation... in fact, let's create our own little int type so that it isn't:
theory Scratch
imports Main
begin
quotient_type int = "nat × nat" / "intrel"
morphisms Rep_Integ Abs_Integ
proof (rule equivpI)
show "reflp intrel" by (auto simp: reflp_def)
show "symp intrel" by (auto simp: symp_def)
show "transp intrel" by (auto simp: transp_def)
qed
lift_definition sub :: "int ⇒ int ⇒ int"
is "λ(x, y) (u, v). (x + v, y + u)"
by auto
lift_definition zero :: "int" is "(0, 0)".
Now, we have
lemma int_inv: "sub n n = zero"
apply transfer
proof (prove)
goal (1 subgoal):
1. ⋀n. intrel ((case n of (x, y) ⇒ λ(u, v). (x + v, y + u)) n) (0, 0)
So, the version we want to prove is
lemma int_inv': "intrel ((case n of (x, y) ⇒ λ(u, v). (x + v, y + u)) n) (0, 0)"
by (induct n) simp
Now we can transfer it with
lemma int_inv: "sub n n = zero"
by transfer (fact int_inv')
Note that the transfer proof method is backtracking — this means that it will try many possible transfers until one of them succeeds. Note however, that this backtracking doesn't apply across separate apply commands. Thus you will always want to write a transfer proof as by transfer something_simple, instead of, say proof transfer.
You can see the many possible versions with
apply transfer
back back back back back
Note also, that if your theorem mentions constants about int which weren't defined with lift_definition, you will need to prove a transfer rule for them separately. There are some examples of that here.
In general, after defining a quotient you will want to "forget" about its underlying construction as soon as possible, proving enough properties by transfer so that the rest can be proven without peeking into your type's construction.
Is it right to say that suppose we have two monotonically increasing functions f,g so that f(n)=Ω(n) and f(g(n))=O(n). Then I want to conclude that g(n)=O(n).
I think that this is a false claim, and I've been trying to provide counter example to show that this is false claim, but after many attempts I'm starting to think otherwise.
Can you please provide some kind of explanation or example if this is a false claim or a way to prove if it's a correct one.
I believe this claim is true. Here's a proof.
Suppose that f(n) = Ω(n). That means that there are constants c, n0 such that
f(n) ≥ cn for any n ≥ n0. (1)
Similarly, since f(g(n)) = O(n), we know that there are constants d, n1 such that
f(g(n)) ≤ dn for any n ≥ n1. (2)
Now, there are two options. The first is that g(n) = O(1), in which case we're done because g(n) is then O(n). The second case is that g(n) ≠ O(1), in which case g grows without bound. That means that there is an n2 such that g(n2) ≥ n0 (g grows without bound, so it eventually overtakes n0) and n2 ≥ n1 (just pick a big n2).
Now, pick any n ≥ n2. Since n ≥ n2, we have that g(n) ≥ g(n2) ≥ n0 because g is monotone increasing, and therefore by (1) we see that
f(g(n)) ≥ cg(n).
Since n ≥ n2 ≥ n1, we can combine this inequality with equation (2) to see that
dn ≥ f(g(n)) ≥ cg(n).
so, in particular, we have that
g(n) ≤ (d / c)n
for all n ≥ n2, so g(n) = O(n).
I'm trying to translate the argument I gave in this answer into Isabelle and I managed to prove it almost completely. However, I still need to prove:
"(∑k | k ∈ {1..n} ∧ d dvd k. f (k/n)) =
(∑q | q ∈ {1..n/d}. f (q/(n/d)))" for d :: nat
My idea was to use this theorem:
sum.reindex_bij_witness
however, I cannot instantiate the transformations i,j that relate the sets S,T of the theorem. In principle, the setting should be:
S = {k. k ∈ {1..n} ∧ d dvd k}
T = {q. q ∈ {1..n/d}}
i k = k/d
j q = q d
I believe there is a typing error. Perhaps I should be using div?
First of all, note that instead of gcd a b = 1, you should write coprime a b. That is equivalent (at least for all types that have a GCD), but it is more convenient to use.
Second, I would not write assumptions like ⋀n. F n = …. It makes more sense to write that as a defines, i.e.
lemma
fixes F :: "nat ⇒ complex"
defines "F ≡ (λn. …)"
Third, {q. q ∈ {1..n/d}} is exactly the same as {1..n/d}, so I suggest you write it that way.
To answer your actual question: If what you have written in your question is how you wrote it in Isabelle and n and d are of type nat, you should be aware that {q. q ∈ {1..n/d}} actually means {1..real n / real d}. If n / d > 1, this is actually an infinite set of real numbers and probably not what you want.
What you actually want is probably the set {1..n div d} where div denotes division on natural numbers. This is then a finite set of natural numbers.
Then you can prove the following fairly easily:
lemma
fixes f :: "real ⇒ complex" and n d :: nat
assumes "d > 0" "d dvd n"
shows "(∑k | k ∈ {1..n} ∧ d dvd k. f (k/n)) =
(∑q∈{1..n div d}. f (q/(n/d)))"
by (rule sum.reindex_bij_witness[of _ "λk. k * d" "λk. k div d"])
(use assms in ‹force simp: div_le_mono›)+
A note on div
div and / denote the same function, namely Rings.divide.divide. However, / for historic reasons (and perhaps in fond memory of Pascal), / additionally imposes the type class restriction inverse, i.e. it only works on types that have an inverse function.
In most practical cases, this means that div is a general kind of division operation on rings, whereas / only works in fields (or division rings, or things that are ‘almost’ fields like formal power series).
If you write a / b for natural numbers a and b, this is therefore a type error. The coercion system of Isabelle then infers that you probably meant to write real a / real b and that's what you get.
It's a good idea to look at the output in such cases to ensure that the inferred coercions match what you intended.
Debugging non-matching rules
If you apply some rule (e.g. with apply (rule …)) and it fails and you don't understand why, there is a little trick to find out. If you add a using [[unify_trace_failure]] before the apply, you get an error message that indicates where exactly the unification failed. In this case, the message is
The following types do not unify:
(nat ⇒ complex) ⇒ nat set ⇒ complex
(real ⇒ complex) ⇒ real set ⇒ complex
This indicates that there is a summation over a set of reals somewhere that should be a summation over a set of naturals.
I would like to prove some basic facts about a datatype_new and a codatatype: the first does not have an infinite element, and that the latter does have one.
theory Co
imports BNF
begin
datatype_new natural = Zero | Successor natural
lemma "¬ (∃ x. x = Successor x)"
oops
codatatype conat = CoZero | CoSucc conat
lemma "∃ x. x = CoSucc x"
oops
The problem was that I could not come up with a pen-and-paper proof, let alone a proof script.
An idea for the first was to use the size function, which has a theorem
size (Successor ?natural) = size ?natural + Suc 0
and somehow using that size is a function, applying it to the two sides of the original equation one cannot have a natural number equal to its successor. But I do not see how I could formalise this.
For the latter I did not even have an idea how to derive this theorem from the facts that the codatatype package proves.
How can I prove these?
Personally, I don't know the first thing about codatatypes. But let me try to help you nevertheless.
The first lemma you posted can be proven automatically by sledgehammer. It finds a proof using the size function, effectively reducing the problem on natural to the same problem on nat:
by (metis Scratch.natural.size(2) n_not_Suc_n nat.size(4) size_nat)
If you want a very basic, step-by-step version of this proof, you could write it like this:
lemma "¬(∃x. x = Successor x)"
proof clarify
fix x assume "x = Successor x"
hence "size x = size (Successor x)" by (rule subst) (rule refl)
also have "... = size x + Suc 0" by (rule natural.size)
finally have "0 = Suc 0" by (subst (asm) add_0_iff) (rule sym)
moreover have "0 ≠ Suc 0" by (rule nat.distinct(1))
ultimately show False by contradiction
qed
If you want a more “elementary” proof, without the use of HOL natural numbers, you can do a proof by contradiction using induction on your natural:
lemma "¬(∃x. x = Successor x)"
proof clarify
fix x assume "x = Successor x"
thus False by (induction x) simp_all
qed
You basically get the two cases in the induction:
Zero = Successor Zero ⟹ False
⋀x. (x = Successor x ⟹ False) ⟹
Successor x = Successor (Successor x) ⟹ False
The first subgoal is a direct consequence of natural.distinct(1), the second one can be reduced to the induction hypothesis using natural.inject. Since these rules are in the simpset, simp_all can solve it automatically.
As for the second lemma, the only solution I can think of is to explicitly construct the infinite element using primcorec:
primcorec infinity :: conat where
"infinity = CoSucc infinity"
Then you can prove your second lemma simply by unfolding the definition:
lemma "∃x. x = CoSucc x"
proof
show "infinity = CoSucc infinity" by (rule infinity.ctr)
qed
Caveat: these proofs work, but I am not sure whether they are the easiest and/or most elegant solution to this problem. I have virtually no knowledge of codatatypes or the new datatype package.
I am trying to understand the recurrence relation of f(n) = n^cos n and g(n) = n. I am told that this relation has no asymptotic behavior related to Big O, little o, Big Omega, little omega, or Theta. Something about the oscillations of cos n? Can I receive a little more understanding on this behavior?
When I use L' Hospital rule on my calculator, I get undefined.
The function ncos n is O(n). Since -1 ≤ cos n ≤ 1, the function ncos n is always bounded between n-1 and n1, so in particular it's always upper-bounded by O(n). However, it's not Ω(n), because for any number n0 and any constant c, you can find an n > n0 where ncos n < cn. One way to do this is to look for choices of n where cos n is negative; the value of n-ε for any ε > 0 will eventually be smaller than cn for any c.
Hope this helps!