My problem: symbolic expression manipulation.
A symbolic expression is built starting from integer constants and variable with the help of operators like +, -, *, /, min,max. More exactly I would represent an expression in the following way (Caml code):
type sym_expr_t =
| PlusInf
| MinusInf
| Const of int
| Var of var_t
| Add of sym_expr_t * sym_expr_t
| Sub of sym_expr_t * sym_expr_t
| Mul of sym_expr_t * sym_expr_t
| Div of sym_expr_t * sym_expr_t
| Min of sym_expr_t * sym_expr_t
| Max of sym_expr_t * sym_expr_t
I imagine that in order to perform useful and efficient computation (eg. a + b - a = 0 or a + 1 > a) I need to have some sort of normal form and operate on it. The above representation will probably not work too good.
Can someone point me out how I should approach this? I don't necessary need code. That can be written easily if I know how. Links to papers that present representations for normal forms and/or algorithms for construction/ simplification/ comparison would also help.
Also, if you know of an Ocaml library that does this let me know.
If you drop out Min and Max, normal forms are easy: they're elements of the field of fractions on your variables, I mean P[Vars]/Q[Vars] where P, Q are polynomials. For Min and Max, I don't know; I suppose the simplest way is to consider them as if/then/else tests, and make them float to the top of your expressions (duplicating stuff in the process), for example P(Max(Q,R)) would be rewritten into P(if Q>R then Q else R), and then in if Q>R then P(Q) else P(R).
I know of two different ways to find normal forms for your expressions expr :
Define rewrite rules expr -> expr that correspond to your intuition, and show that they are normalizing. That can be done by directing the equations that you know are true : from Add(a,Add(b,c)) = Add(Add(a,b),c) you will derive either Add(a,Add(b,c)) -> Add(Add(a,b),c) or the other way around. But then you have an equation system for which you need to show Church-Rosser and normalization; dirty business indeed.
Take a more semantic approach of giving a "semantic" of your values : an element in expr is really a notation for a mathematical object that lives in the type sem. Find a suitable (unique) representation for objects of sem, then an evaluation function expr -> sem, then finally (if you wish to, but you don't need to for equality checking for example) a reification sem -> expr. The composition of both transformations will naturally give you a normalization procedure, without having to worry for example about direction of the Add rewriting (some arbitrary choice will arise naturally from your reification function). For example, for polynomial fractions, the semantic space would be something like:
.
type sem = poly * poly
and poly = (multiplicity * var * degree) list
and multiplicity = int
and degree = int
Of course, this is not always so easy. I don't see right know what representation give to a semantic space with Min and Max functions.
Edit: Regarding external libraries, I don't know any and I'm not sure there are. You should maybe look for bindings to other symbolic algebra software, but I haven't heard of it (there was a Jane Street Summer Project about that a few years ago, but I'm not sure there was any deliverable produced).
If you need that for a production application, maybe you should directly consider writing the binding yourselves, eg. to Sage or Maxima. I don't know what it would be like.
The usual approach to such a problem is:
Start with a string, such a as "a + 1 > a"
Go through a lexer, and separate your input into distinct tokens: [Variable('a'); Plus; Number(1); GreaterThan; Variable('a')]
Parse the tokens into a syntax tree (what you have now). This is where you use the operator precedence rules: Max( Add( Var('a'), Const(1)), Var('a'))
Make a function that can interpret the syntax tree to obtain your final result
let eval_expr expr = match expr with
| Number n -> n
| Add a b -> (eval_expr a) + (eval_expr b)
...
Pardon the syntax, I haven't used Ocaml in a while.
About libraries, I don't remember any out of the top of my mind, but there certainly are good ones easily available - this is the kind of task that the FP community loves doing.
Related
I would like to solve problems combining boolean and integer logic in linear arithmetic with a SAT/SMT solver. At first glance, Z3 seems promising.
First of all, is it at all possible to solve the following problem? This answer makes it seem like it works.
int x,y,z
boolean a,b,c
( (3x + y - 2z >= 10) OR (A AND (NOT B OR C)) OR ((A == C) AND (x + y >= 5)) )
If so, how does Z3 solve this kind of problem in theory and is there any documentation about it?
I could think of two ways to solve this problem. One would be to convert the Boolean operations into a linear integer expression. Another solution I read about is to use the Nelson-Oppen Combination Method described in [Kro 08].
I found a corresponding documentation in chapter 3.2.2. Solving Arithmetical Fragments, Table 1 a listing of the implemented algorithms for a certain logic.
Yes, SMT solvers are quite good at solving problems of this sort. Your problem can be expressed using z3's Python interface like this:
from z3 import *
x, y, z = Ints('x y z')
A, B, C = Bools('A B C')
solve (Or(3*x + y - 2*z >= 10
, And(A, Or(Not(B), C))
, And(A == C, x + y >= 5)))
This prints:
[A = True, z = 3, y = 0, B = True, C = True, x = 5]
giving you a (not necessarily "the") model that satisfies your constraints.
SMT solvers can deal with integers, machine words (i.e., bit-vectors), reals, along with many other data types, and there are efficient procedures for combinations of linear-integer-arithmetic, booleans, uninterpreted-functions, bit-vectors amongst many others.
See http://smtlib.cs.uiowa.edu for many resources on SMT solving, including references to other work. Any given solver (i.e., z3, yices, cvc etc.) will be a collection of various algorithms, heuristics and tactics. It's hard to compare them directly as each shine in their own way for certain sublogics, but for the base set of linear-integer arithmetic, booleans, and bit-vectors, they should all perform fairly well. Looks like you already found some good references, so you can do further reading as necessary; though for most end users it's neither necessary nor that important to know how an SMT solver internally works.
I have a source file like (without loss of generality (only to image a possible syntax)):
function a()
return g // global variable without any internal structure exactly
end
function b(x, y)
local z = x * y
return z + 1
end
function c(z, t)
return b(z * z, a())
end
// ...etc
I want to defferentiate any function WRT to some variable.
All the formal parametres we can treat as a functions with unknown at derive time internal structure.
If I stand correct further, then the following is truth (for depending symbols ' is part of symbol, for global variables is operator during substitute time stage (def: g{g} is one, but g{y} is zero)):
function a'()
return g';
end
function b'(x, y, x', y')
local z' = x' * y + x * y'
return z' + 0
end
But what to do with last function? Namely, with actual parameters in substitution of function b?
Is there any ready to use implementations of general algorithm to work with the above? What to do with higher order derivatives (especially interesting, how to handle the formal parameters)? Are there any other possible unclear cases?
I would suggest having your parameters be symbolic expressions that know how to respond to derivatives, and having all operations take functions and return functions. Then you will get a final expression that knows how to be represented as a derivative. Furthermore you can do things like partial derivatives at a later point because you have the symbolic expression.
For a real example of what I mean, see http://www.elem.com/~btilly/kelly-criterion/js/advanced-math.js for a library that I wrote to solve a calculus problem in JavaScript, and search for "Optimize if requested" in the source for http://www.elem.com/~btilly/kelly-criterion/betting-returns2.html to see how I used it. See http://www.elem.com/~btilly/kelly-criterion/ for an explanation of why I was writing that code.
In that example I, of course, was not working from infix notation. But that is a standard parsing problem that I think you know how to solve.
I am solving the Programming assinment for Harvard CS 51 programming course in ocaml.
The problem is to define a function that can compress a list of chars to list of pairs where each pair contains a number of consequent occurencies of the character in the list and the character itself, i.e. after applying this function to the list ['a';'a';'a';'a';'a';'b';'b';'b';'c';'d';'d';'d';'d'] we should get the list of [(5,'a');(3,'b');(1,'c');(4,'d')].
I came up with the function that uses auxiliary function go to solve this problem:
let to_run_length (lst : char list) : (int*char) list =
let rec go i s lst1 =
match lst1 with
| [] -> [(i,s)]
| (x::xs) when s <> x -> (i,s) :: go 0 x lst1
| (x::xs) -> go (i + 1) s xs
in match lst with
| x :: xs -> go 0 x lst
| [] -> []
My question is: Is it possible to define recursive function to_run_length with nested pattern matching without defining an auxiliary function go. How in this case we can store a state of counter of already passed elements?
The way you have implemented to_run_length is correct, readable and efficient. It is a good solution. (only nitpick: the indentation after in is wrong)
If you want to avoid the intermediary function, you must use the information present in the return from the recursive call instead. This can be described in a slightly more abstract way:
the run length encoding of the empty list is the empty list
the run length encoding of the list x::xs is,
if the run length encoding of xs start with x, then ...
if it doesn't, then (x,1) ::run length encoding of xs
(I intentionally do not provide source code to let you work the detail out, but unfortunately there is not much to hide with such relatively simple functions.)
Food for thought: You usually encounter this kind of techniques when considering tail-recursive and non-tail-recursive functions (what I've done resembles turning a tail-rec function in non-tail-rec form). In this particular case, your original function was not tail recursive. A function is tail-recursive when the flows of arguments/results only goes "down" the recursive calls (you return them, rather than reusing them to build a larger result). In my function, the flow of arguments/results only goes "up" the recursive calls (the calls have the least information possible, and all the code logic is done by inspecting the results). In your implementation, flows goes both "down" (the integer counter) and "up" (the encoded result).
Edit: upon request of the original poster, here is my solution:
let rec run_length = function
| [] -> []
| x::xs ->
match run_length xs with
| (n,y)::ys when x = y -> (n+1,x)::ys
| res -> (1,x)::res
I don't think it is a good idea to write this function. Current solution is OK.
But if you still want to do it you can use one of two approaches.
1) Without changing arguments of your function. You can define some toplevel mutable values which will contain accumulators which are used in your auxilary function now.
2) You can add argument to your function to store some data. You can find some examples when googling for continuation-passing style.
Happy hacking!
P.S. I still want to underline that your current solution is OK and you don't need to improve it!
Lets say, that problems are fairly simple - something, that pre-degree theoretical physics student would solve. And student does the hardest part of the task - functional reading: parsing linguistically free form text, to get input and output variables and input variable values.
For example: a problem about kinematic equations, where there are variables {a,d,t,va,vf} and few functions that describe, how thy are dependent of each-other. So using skills acquired in playing fitting blocks where thy fit, you play with the equations to get the output variable you where looking for.
In any case, there are exactly 2 possible outputs you might want and thy are (with working example):
1) Equation for that variable
Physics[have_, find_] := Solve[Flatten[{
d == vf * t - (a * t^2) /2, (* etc. *)
have }], find]
Physics[True, {d}]
{{d -> (1/2)*(2*t*vf - a*t^2)}}
2) Exact or general numerical value for that variable
Physics[have_, find_] := Solve[Flatten[{
d == vf * t - (a * t^2) /2, (* etc. *)
have }], find]
Physics[{t == 9.7, vf == -104.98, a == -9.8}, {d}]
{{d->-557.265}}
I am not sure, that I am approaching the problem correctly.
I think that I would probably prefer an approach like
In[1]:= Physics[find_, have_:{}] := Solve[
{d == vf*t - (a*t^2)/2 (* , etc *)} /. have, find]
In[2]:= Physics[d]
Out[2]= {{d -> 1/2 (-a t^2 + 2 t vf)}}
In[2]:= Physics[d, {t -> 9.7, vf -> -104.98, a -> -9.8}]
Out[2]= {{d -> -557.265}}
Where the have variables are given as a list of replacement rules.
As an aside, in these types of physics problems, a nice thing to do is define your physical constants like
N[g] = -9.8;
which produces a NValues for g. Then
N[tf] = 9.7;N[vf] = -104.98;
Physics[d, {t -> tf, vf -> vf, a -> g}]
%//N
produces
{{d->1/2 (-g tf^2+2 tf vf)}}
{{d->-557.265}}
Let me show some advanges of Simon's approach:
You are at least approaching this problem reasonably. I see a fine general purpose function and I see you're getting results, which is what matters primarily. There is no 'correct' solution, since there might be a large range of acceptable solutions. In some scenario's some solutions may be preferred over others, for instance because of performance, while that might be the other way around in other scenarios.
The only slight problem I have with your example is the dubious parametername 'have'.
Why do you think this would be a wrong approach?
I'm trying to fully understand all of Haskell's concepts.
In what ways are algebraic data types similar to generic types, e.g., in C# and Java? And how are they different? What's so algebraic about them anyway?
I'm familiar with universal algebra and its rings and fields, but I only have a vague idea of how Haskell's types work.
Haskell's algebraic data types are named such since they correspond to an initial algebra in category theory, giving us some laws, some operations and some symbols to manipulate. We may even use algebraic notation for describing regular data structures, where:
+ represents sum types (disjoint unions, e.g. Either).
• represents product types (e.g. structs or tuples)
X for the singleton type (e.g. data X a = X a)
1 for the unit type ()
and μ for the least fixed point (e.g. recursive types), usually implicit.
with some additional notation:
X² for X•X
In fact, you might say (following Brent Yorgey) that a Haskell data type is regular if it can be expressed in terms of 1, X, +, •, and a least fixed point.
With this notation, we can concisely describe many regular data structures:
Units: data () = ()
1
Options: data Maybe a = Nothing | Just a
1 + X
Lists: data [a] = [] | a : [a]
L = 1+X•L
Binary trees: data BTree a = Empty | Node a (BTree a) (BTree a)
B = 1 + X•B²
Other operations hold (taken from Brent Yorgey's paper, listed in the references):
Expansion: unfolding the fix point can be helpful for thinking about lists. L = 1 + X + X² + X³ + ... (that is, lists are either empty, or they have one element, or two elements, or three, or ...)
Composition, ◦, given types F and G, the composition F ◦ G is a type which builds “F-structures made out of G-structures” (e.g. R = X • (L ◦ R) ,where L is lists, is a rose tree.
Differentiation, the derivative of a data type D (given as D') is the type of D-structures with a single “hole”, that is, a distinguished location not containing any data. That amazingly satisfy the same rules as for differentiation in calculus:
1′ = 0
X′ = 1
(F + G)′ = F' + G′
(F • G)′ = F • G′ + F′ • G
(F ◦ G)′ = (F′ ◦ G) • G′
References:
Species and Functors and Types, Oh My!, Brent A. Yorgey, Haskell’10, September 30, 2010, Baltimore, Maryland, USA
Clowns to the left of me, jokers to the right (Dissecting Data Structures), Conor McBride POPL 2008
"Algebraic Data Types" in Haskell support full parametric polymorphism, which is the more technically correct name for generics, as a simple example the list data type:
data List a = Cons a (List a) | Nil
Is equivalent (as much as is possible, and ignoring non-strict evaluation, etc) to
class List<a> {
class Cons : List<a> {
a head;
List<a> tail;
}
class Nil : List<a> {}
}
Of course Haskell's type system allows more ... interesting use of type parameters but this is just a simple example. With regards to the "Algebraic Type" name, i've honestly never been entirely sure of the exact reason for them being named that, but have assumed that it's due the mathematical underpinnings of the type system. I believe that the reason boils down to the theoretical definition of an ADT being the "product of a set of constructors", however it's been a couple of years since i escaped university so i can no longer remember the specifics.
[Edit: Thanks to Chris Conway for pointing out my foolish error, ADT are of course sum types, the constructors providing the product/tuple of fields]
In universal algebra
an algebra consists of some sets of elements
(think of each set as the set of values of a type)
and some operations, which map elements to elements.
For example, suppose you have a type of "list elements" and a
type of "lists". As operations you have the "empty list", which is a 0-argument
function returning a "list", and a "cons" function which takes two arguments,
a "list element" and a "list", and produce a "list".
At this point there are many algebras that fit the description,
as two undesirable things may happen:
There could be elements in the "list" set which cannot be built
from the "empty list" and the "cons operation", so-called "junk".
This could be lists starting from some element that fell from the sky,
or loops without a beginning, or infinite lists.
The results of "cons" applied to different arguments could be equal,
e.g. consing an element to a non-empty list
could be equal to the empty list. This is sometimes called "confusion".
An algebra which has neither of these undesirable properties is called
initial, and this is the intended meaning of the abstract data type.
The name initial derives from the property that there is exactly
one homomorphism from the initial algebra to any given algebra.
Essentially you can evaluate the value of a list by applying the operations
in the other algebra, and the result is well-defined.
It gets more complicated for polymorphic types ...
A simple reason why they are called algebraic; there are both sum (logical disjunction) and product (logical conjunction) types. A sum type is a discriminated union, e.g:
data Bool = False | True
A product type is a type with multiple parameters:
data Pair a b = Pair a b
In O'Caml "product" is made more explicit:
type 'a 'b pair = Pair of 'a * 'b
Haskell's datatypes are called "algebraic" because of their connection to categorical initial algebras. But that way lies madness.
#olliej: ADTs are actually "sum" types. Tuples are products.
#Timbo:
You are basically right about it being sort of like an abstract Tree class with three derived classes (Empty, Leaf, and Node), but you would also need to enforce the guarantee that some one using your Tree class can never add any new derived classes, since the strategy for using the Tree datat type is to write code that switches at runtime based on the type of each element in the tree (and adding new derived types would break existing code). You can sort of imagine this getting nasty in C# or C++, but in Haskell, ML, and OCaml, this is central to the language design and syntax so coding style supports it in a much more convenient manner, via pattern matching.
ADT (sum types) are also sort of like tagged unions or variant types in C or C++.
old question, but no one's mentioned nullability, which is an important aspect of Algebraic Data Types, perhaps the most important aspect. Since each value most be one of alternatives, exhaustive case-based pattern matching is possible.
For me, the concept of Haskell's algebraic data types always looked like polymorphism in OO-languages like C#.
Look at the example from http://en.wikipedia.org/wiki/Algebraic_data_types:
data Tree = Empty
| Leaf Int
| Node Tree Tree
This could be implemented in C# as a TreeNode base class, with a derived Leaf class and a derived TreeNodeWithChildren class, and if you want even a derived EmptyNode class.
(OK I know, nobody would ever do that, but at least you could do it.)