This logic programming is really making a lap dance on my imperative programming skills. This is homework, so please just don't drop me the answer. This is what I have:
fibo(N,1) :-
N < 2,
!.
fibo(N,R) :-
N1 is N-1,
N2 is N-2,
fibo(N1,R1),
fibo(N2,R2),
R is R1+R2.
I'm suppose to make another function that looks like this; fib(N,Value,LastValue).
N is the n'th number, and value is the return value. I don't understand how I can rewrite this using accumulation. And since it counts backwards I don't see how it can "know" a last value before it calculates anything. :s Any input is appreciated.
I could post here the solution, but since that this is homework, it would be counter-productive. Instead, here's a lead:
The problem with the version of Fibonacci that you listed is that it is inefficient. Each call to fibo/2 causes another two calls, but some of these calls calculate the values of the same Fibonacci numbers. For example, in pseudo-code:
(a) fibo(4) -> fibo(3), fibo(2)
(b) fibo(3) -> fibo(2), fibo(1)
(c) fibo(2) -> fibo(1), fibo(0) % called from (a)
(d) fibo(2) -> fibo(1), fibo(0) % called from (b), redundant
To overcome this deficiency, you were asked to rephrase Fibonacci in terms of returning not just the last value, but the last two values, so that each call to fib/3 will cause only a single recursive call (hence calculate the Fibonacci series in linear time). You'll need to change the base cases to:
fib(1,1,0).
fib(2,1,1).
I'll leave the recursive case to you.
For the impatient
Here is the recursive case as well:
fib(N, Val, Last) :-
N > 2,
N1 is N - 1,
fib(N1, Last, Last1), % single call with two output arguments,
% instead of two calls with one output argument
Val is Last + Last1.
See the related discussion:
Generalizing Fibonacci sequence with SICStus Prolog
and consider ony's very good solution using finite domain constraints from there.
Perhaps using tail recursion is a good option
edit:
Instead of breaking fib(6) into fib(5)+fib(4) you might try something like fib(6) = fib(6,0,0) the first parameter is the count of steps, when it reaches 0 you stop, the second parameter the last value you calculated, and the third parameter is the value to calculate which is equal to the sum of current second and third parameters (with the exception of the first step, in which 0 + 0 will be 1)
So to calculate you set the second parameter at each call and acumulate in the third so fib(6,0,0) => fib(5,0,1) => fib(4,1,1) => fib(3,1,2) => fib(2,2,3) => fib(1,3,5) => fib(0,5,8) then you return 8
In that method you dont actually have to save in the stack the adress return, avoiding stack overflow
Remember that there is another way to calculate the Fibonacci sequence: starting from the base case and moving up.
Right now, to calculate fib(n), you add fib(n-1) and fib(n-2). Instead, flip that around and calculate fib(0) and fib(1) based on the definition of the Fibonacci sequence, and build up from that.
You almost already have it. Just rewrite:
fibo(N, Value) :-
N1 is N-1, N2 is N-2,
fibo(N1, LastValue),fibo(N2, SecondToLastValue),
Value is LastValue + SecondToLastValue.
in terms of
fibo2(N, Value, LastValue):- ...
I don't understand how I can rewrite
this using accumulation
Just don't, this is not needed (although it's possible to do so).
Related
So I'm trying to code something for my class that will output an int list of the first k terms in an n-step fibonacci sequence.
So for those of you that don't know, an n step fibonacci sequence is when you add the n numbers before it to get the next one,
so for n=1 it'd be 1,1,1,1,1,...
n=2, it'd be 1,1,2,3,5,...
n=3 it'd be 1,1,2,4,7...
My approach was to start of with a base case, so
let rec n_step n k=
if k=1 then [1] else
if n=1 then 1::nacci n k-1 else
but now I'm stuck here. I know I need to iterate through and add up the terms in the list, but I'm not sure how to accomplish this.
I made a helper function sum
let rec sum lst =
match lst with
| [] -> 0
| h::t -> h + sum t
I was trying to make it selectively add the last n numbers of the list to get the next value but I got stuck on that as well
Thanks in advance!
This is homework so I'll only suggest some steps, not a complete solution:
if you come from an imperative background, try first an imperative solution such as a loop that prints the result instead of building a list and tying the recursion at once. You can store the required state in a global and gradually change that to pass parameters.
start with n=2, using a tuple instead of a list. It will be considerably easier to do that first, manually extend to fixed n=3 and fixed n=4 before using a list.
Applying these two pieces of advice, a first step might be:
let print_fib_2 () =
let previous_ref = ref (1, 1) in
for i = 0 to 10 do
let (a, b) = !previous_ref in
let next = a + b in
previous_ref := (next, a);
Printf.printf "%d\n" next
done
I'd first generalize to using changing n=2 to n=3: what happens to the pairs (a, b) and (next, a)? what does it mean in terms of lists?
By following baby steps from a working example you should be able to work to a solution.
I've figured it out using many helper functions.
So I've made a sum function that would sum up the values in a list
A gather function that would only take the n values from the entire list
and then in my original function, I used pattern matching for the k's such that if k=1, it would yield one and otherwise it would recurse. If it wasn't one I appended the next value using the helper
I'm working on a homework problem that asks me this:
Tiven a finite set of numbers, and a target number, find if the set can be used to calculate the target number using basic math operations (add, sub, mult, div) and using each number in the set exactly once (so I need to exhaust the set). This has to be done with recursion.
So, for example, if I have the set
{1, 2, 3, 4}
and target 10, then I could get to it by using
((3 * 4) - 2)/1 = 10.
I'm trying to phrase the algorithm in pseudo-code, but so far haven't gotten too far. I'm thinking graphs are the way to go, but would definitely appreciate help on this. thanks.
This isn't meant to be the fastest solution, but rather an instructive one.
It recursively generates all equations in postfix notation
It also provides a translation from postfix to infix notation
There is no actual arithmetic calculation done, so you have to implement that on your own
Be careful about division by zero
With 4 operands, 4 possible operators, it generates all 7680 = 5 * 4! * 4^3
possible expressions.
5 is Catalan(3). Catalan(N) is the number of ways to paranthesize N+1 operands.
4! because the 4 operands are permutable
4^3 because the 3 operators each have 4 choice
This definitely does not scale well, as the number of expressions for N operands is [1, 8, 192, 7680, 430080, 30965760, 2724986880, ...].
In general, if you have n+1 operands, and must insert n operators chosen from k possibilities, then there are (2n)!/n! k^n possible equations.
Good luck!
import java.util.*;
public class Expressions {
static String operators = "+-/*";
static String translate(String postfix) {
Stack<String> expr = new Stack<String>();
Scanner sc = new Scanner(postfix);
while (sc.hasNext()) {
String t = sc.next();
if (operators.indexOf(t) == -1) {
expr.push(t);
} else {
expr.push("(" + expr.pop() + t + expr.pop() + ")");
}
}
return expr.pop();
}
static void brute(Integer[] numbers, int stackHeight, String eq) {
if (stackHeight >= 2) {
for (char op : operators.toCharArray()) {
brute(numbers, stackHeight - 1, eq + " " + op);
}
}
boolean allUsedUp = true;
for (int i = 0; i < numbers.length; i++) {
if (numbers[i] != null) {
allUsedUp = false;
Integer n = numbers[i];
numbers[i] = null;
brute(numbers, stackHeight + 1, eq + " " + n);
numbers[i] = n;
}
}
if (allUsedUp && stackHeight == 1) {
System.out.println(eq + " === " + translate(eq));
}
}
static void expression(Integer... numbers) {
brute(numbers, 0, "");
}
public static void main(String args[]) {
expression(1, 2, 3, 4);
}
}
Before thinking about how to solve the problem (like with graphs), it really helps to just look at the problem. If you find yourself stuck and can't seem to come up with any pseudo-code, then most likely there is something that is holding you back; Some other question or concern that hasn't been addressed yet. An example 'sticky' question in this case might be, "What exactly is recursive about this problem?"
Before you read the next paragraph, try to answer this question first. If you knew what was recursive about the problem, then writing a recursive method to solve it might not be very difficult.
You want to know if some expression that uses a set of numbers (each number used only once) gives you a target value. There are four binary operations, each with an inverse. So, in other words, you want to know if the first number operated with some expression of the other numbers gives you the target. Well, in other words, you want to know if some expression of the 'other' numbers is [...]. If not, then using the first operation with the first number doesn't really give you what you need, so try the other ops. If they don't work, then maybe it just wasn't meant to be.
Edit: I thought of this for an infix expression of four operators without parenthesis, since a comment on the original question said that parenthesis were added for the sake of an example (for clarity?) and the use of parenthesis was not explicitly stated.
Well, you didn't mention efficiency so I'm going to post a really brute force solution and let you optimize it if you want to. Since you can have parantheses, it's easy to brute force it using Reverse Polish Notation:
First of all, if your set has n numbers, you must use exactly n - 1 operators. So your solution will be given by a sequence of 2n - 1 symbols from {{your given set}, {*, /, +, -}}
st = a stack of length 2n - 1
n = numbers in your set
a = your set, to which you add *, /, +, -
v[i] = 1 if the NUMBER i has been used before, 0 otherwise
void go(int k)
{
if ( k > 2n - 1 )
{
// eval st as described on Wikipedia.
// Careful though, it might not be valid, so you'll have to check that it is
// if it evals to your target value great, you can build your target from the given numbers. Otherwise, go on.
return;
}
for ( each symbol x in a )
if ( x isn't a number or x is a number but v[x] isn't 1 )
{
st[k] = x;
if ( x is a number )
v[x] = 1;
go(k + 1);
}
}
Generally speaking, when you need to do something recursively it helps to start from the "bottom" and think your way up.
Consider: You have a set S of n numbers {a,b,c,...}, and a set of four operations {+,-,*,/}. Let's call your recursive function that operates on the set F(S)
If n is 1, then F(S) will just be that number.
If n is 2, F(S) can be eight things:
pick your left-hand number from S (2 choices)
then pick an operation to apply (4 choices)
your right-hand number will be whatever is left in the set
Now, you can generalize from the n=2 case:
Pick a number x from S to be the left-hand operand (n choices)
Pick an operation to apply
your right hand number will be F(S-x)
I'll let you take it from here. :)
edit: Mark poses a valid criticism; the above method won't get absolutely everything. To fix that problem, you need to think about it in a slightly different way:
At each step, you first pick an operation (4 choices), and then
partition S into two sets, for the left and right hand operands,
and recursively apply F to both partitions
Finding all partitions of a set into 2 parts isn't trivial itself, though.
Your best clue about how to approach this problem is the fact that your teacher/professor wants you to use recursion. That is, this isn't a math problem - it is a search problem.
Not to give too much away (it is homework after all), but you have to spawn a call to the recursive function using an operator, a number and a list containing the remaining numbers. The recursive function will extract a number from the list and, using the operation passed in, combine it with the number passed in (which is your running total). Take the running total and call yourself again with the remaining items on the list (you'll have to iterate the list within the call but the sequence of calls is depth-first). Do this once for each of the four operators unless Success has been achieved by a previous leg of the search.
I updated this to use a list instead of a stack
When the result of the operation is your target number and your list is empty, then you have successfully found the set of operations (those that traced the path to the successful leaf) - set the Success flag and unwind. Note that the operators aren't on a list nor are they in the call: the function itself always iterates over all four. Your mechanism for "unwinding" the operator sequence from the successful leaf to get the sequence is to return the current operator and number prepended to the value returned by recursive call (only one of which will be successful since you stop at success - that, obviously, is the one to use). If none are successful, then what you return isn't important anyhow.
Update This is much harder when you have to consider expressions like the one that Daniel posted. You have combinatorics on the numbers and the groupings (numbers due to the fact that / and - are order sensitive even without grouping and grouping because it changes precedence). Then, of course, you also have the combinatorics of the operations. It is harder to manage the differences between (4 + 3) * 2 and 4 + (3 * 2) because grouping doesn't recurse like operators or numbers (which you can just iterate over in a breadth-first manner while making your (depth-first) recursive calls).
Here's some Python code to get you started: it just prints all the possible expressions, without worrying too much about redundancy. You'd need to modify it to evaluate expressions and compare to the target number, rather than simply printing them.
The basic idea is: given a set S of numbers, partition S into two subsets left and right in all possible ways (where we don't care about the order or the elements in left and right), such that left and right are both nonempty. Now for each of these partitions, find all ways of combining the elements in left (recursively!), and similarly for right, and combine the two resulting values with all possible operators. The recursion bottoms out when a set has just one element, in which case there's only one value possible.
Even if you don't know Python, the expressions function should be reasonably easy to follow; the splittings function contains some Python oddities, but all it does is to find all the partitions of the list l into left and right pieces.
def splittings(l):
n = len(l)
for i in xrange(2**n):
left = [e for b, e in enumerate(l) if i & 2**b]
right = [e for b, e in enumerate(l) if not i & 2**b]
yield left, right
def expressions(l):
if len(l) == 1:
yield l[0]
else:
for left, right in splittings(l):
if not left or not right:
continue
for el in expressions(left):
for er in expressions(right):
for operator in '+-*/':
yield '(' + el + operator + er + ')'
for x in expressions('1234'):
print x
pusedo code:
Works(list, target)
for n in list
tmp=list.remove(n)
return Works(tmp,target+n) or Works(tmp,target-n) or Works(tmp, n-target) or ...
then you just have to put the base case in. I think I gave away to much.
I need an algorithm to find, what I call, "ordered combinations" (Maybe someone knows the real name for this if there is one).
Of course I already tried to come up with an algorithm on my own but I'm really stuck.
How it should work:
Given 2 lists (not sets, order is important here!) of elements that are guaranteed to contain the same elements, all ordered combinations.
An ordered combination is a 2-tuple, 3-tuple, ... n-tuple (no limit on N) of elements that appear in the same order in both lists.
Its entirely possible that an element occurs more than once in a list.
But every element from one list is guaranteed to appear at least once in the other list.
It does not matter if the output contains a combination more than once.
I'm not really sure if that makes it clear so here are multiple examples:
(List1, List2, Expected Result, Annotation)
ASDF
ADSF
Result: AS, AD, AF, SF, DF, ASF, ADF
Note: ASD is not a valid result because there is no way to have ascending indices in the second list for this combination
ADSD
ASDD
Result: AD, AS, AD, DD, SD, ASD, ADD
Note: AD appears twice because it can be created from indices 1,2 and 1,4 and in the second list 1,3 and 1,4. But it would also be correct if it only appears once. Also D appears twice in both lists in an order, so this allows ADD as a valid combination too.
SDFG
SDFG
Result: SD, SF, SG, DF, DG, FG, SDF, SFG, SDG, DFG, SDFG,
Note: Same input; all combinations are possible
ABCDEFG
GFEDCBA
Result: <empty>
Note: There are no combinations that appear in the same order in both lists
QWRRRRRRR
WRQ
Result: WR
Note: The only combination that appears in the same order in both sets is WR
Notes:
While it's a language agnostic algorithm I'd prefer answers that contain either C# or pseudo-code so I can understand them.
I realized that longer combinations are always made up from shorter combinations. Example: SDF can only be a valid result if SD and DF are possible too. Maybe this helps to make the algorithm more performant by building the longer combinations from the shorter ones.
Speed is of great importance here. This is algorithm will be used in realtime!
If it's not clear how the algorithm works, drop a comment. I'll add an example to clarify it.
Maybe this problem is already known and solved, but I don't know the proper name for it.
I would describe this problem as enumerating common subsequences of two strings. As a first cut, make a method like this, which chooses the first letter nondeterministically and recurses (Python, sorry).
def commonsubseqs(word1, word2, prefix=''):
if len(prefix) >= 2:
print(prefix)
for letter in set(word1) & set(word2): # set intersection
# figure out what's left after consuming the first instance of letter
remainder1 = word1[word1.index(letter) + 1:]
remainder2 = word2[word2.index(letter) + 1:]
# take letter and recurse
commonsubseqs(remainder1, remainder2, prefix + letter)
If this simple solution is not fast enough for you, then it can be improved as follows. For each pair of suffixes of the two words, we precompute the list of recursive calls. In Python again:
def commonsubseqshelper(table, prefix, i, j):
if len(prefix) >= 2:
print(''.join(prefix))
for (letter, i1, j1) in table[i][j]:
prefix.append(letter)
commonsubseqshelper(table, prefix, i1, j1)
del prefix[-1] # delete the last item
def commonsubseqs(word1, word2):
table = [[[(letter, word1.index(letter, i) + 1, word2.index(letter, j) + 1)
for letter in set(word1[i:]) & set(word2[j:])]
for j in range(len(word2) + 1)] # 0..len(word2)
for i in range(len(word1) + 1)] # 0..len(word1)
commonsubseqshelper(table, [], 0, 0)
This polynomial-time preprocessing step improves the speed of enumeration to its asymptotic optimum.
maple code, no matter write this matrix in procedure or not, still get error, how to summation to infinity
DetAn:= (n)-> LinearAlgebra:-Determinant(
Matrix(
n, n,
(i,j)->
if j >= i and (j-i)::even then
(j-i+1)*(j-1)!/(i-1)!*a(j-i+1)*x
elif i-j = 1 then -1
else 0
end if
)
):
Summation(DetAn(k)*z^k/k!, k=0..infinity);
Update:
a(i) could be a := t -> t^2
You will get an error for the given input because the sum (or Summation) command has normal evaluation rules for procedure arguments and so will try to evaluate DetAn(n) for nonumeric symbolic n. You'd get the same error message (from the Matrix constructor) if you just called,
DetAn(n);
where n is an unassigned name.
But delaying that premature evaluation isn't going to get a result.
Summation('DetAn'(k)*z^k/k!, k=0..infinity);
LinearAlgebra:-Determinant is not going to cough up a closed form result for symbolic n. You can get a recursive summation formula for DetAn(n), ie. as a sum of terms involving DetAn(j-1) or DetAn(j-2) from j=1..n/2. I don't know whether you could hammer on that for a generating function.
Consider what kind of answer you are looking for, if only from the Determinant call. Are hoping for a nested sum (nested to a fixed, finite depth)?
What is a(i)?
Why is the determinant in terms of powers of x, while z comes into the summation terms?
Mathematica can simply take infintiy as a limit:
Sum[(1/2)^i, {i, 0, Infinity}]
Out= 2
I didn't try with your example but its worth a shot.
I'm working on a homework problem that asks me this:
Tiven a finite set of numbers, and a target number, find if the set can be used to calculate the target number using basic math operations (add, sub, mult, div) and using each number in the set exactly once (so I need to exhaust the set). This has to be done with recursion.
So, for example, if I have the set
{1, 2, 3, 4}
and target 10, then I could get to it by using
((3 * 4) - 2)/1 = 10.
I'm trying to phrase the algorithm in pseudo-code, but so far haven't gotten too far. I'm thinking graphs are the way to go, but would definitely appreciate help on this. thanks.
This isn't meant to be the fastest solution, but rather an instructive one.
It recursively generates all equations in postfix notation
It also provides a translation from postfix to infix notation
There is no actual arithmetic calculation done, so you have to implement that on your own
Be careful about division by zero
With 4 operands, 4 possible operators, it generates all 7680 = 5 * 4! * 4^3
possible expressions.
5 is Catalan(3). Catalan(N) is the number of ways to paranthesize N+1 operands.
4! because the 4 operands are permutable
4^3 because the 3 operators each have 4 choice
This definitely does not scale well, as the number of expressions for N operands is [1, 8, 192, 7680, 430080, 30965760, 2724986880, ...].
In general, if you have n+1 operands, and must insert n operators chosen from k possibilities, then there are (2n)!/n! k^n possible equations.
Good luck!
import java.util.*;
public class Expressions {
static String operators = "+-/*";
static String translate(String postfix) {
Stack<String> expr = new Stack<String>();
Scanner sc = new Scanner(postfix);
while (sc.hasNext()) {
String t = sc.next();
if (operators.indexOf(t) == -1) {
expr.push(t);
} else {
expr.push("(" + expr.pop() + t + expr.pop() + ")");
}
}
return expr.pop();
}
static void brute(Integer[] numbers, int stackHeight, String eq) {
if (stackHeight >= 2) {
for (char op : operators.toCharArray()) {
brute(numbers, stackHeight - 1, eq + " " + op);
}
}
boolean allUsedUp = true;
for (int i = 0; i < numbers.length; i++) {
if (numbers[i] != null) {
allUsedUp = false;
Integer n = numbers[i];
numbers[i] = null;
brute(numbers, stackHeight + 1, eq + " " + n);
numbers[i] = n;
}
}
if (allUsedUp && stackHeight == 1) {
System.out.println(eq + " === " + translate(eq));
}
}
static void expression(Integer... numbers) {
brute(numbers, 0, "");
}
public static void main(String args[]) {
expression(1, 2, 3, 4);
}
}
Before thinking about how to solve the problem (like with graphs), it really helps to just look at the problem. If you find yourself stuck and can't seem to come up with any pseudo-code, then most likely there is something that is holding you back; Some other question or concern that hasn't been addressed yet. An example 'sticky' question in this case might be, "What exactly is recursive about this problem?"
Before you read the next paragraph, try to answer this question first. If you knew what was recursive about the problem, then writing a recursive method to solve it might not be very difficult.
You want to know if some expression that uses a set of numbers (each number used only once) gives you a target value. There are four binary operations, each with an inverse. So, in other words, you want to know if the first number operated with some expression of the other numbers gives you the target. Well, in other words, you want to know if some expression of the 'other' numbers is [...]. If not, then using the first operation with the first number doesn't really give you what you need, so try the other ops. If they don't work, then maybe it just wasn't meant to be.
Edit: I thought of this for an infix expression of four operators without parenthesis, since a comment on the original question said that parenthesis were added for the sake of an example (for clarity?) and the use of parenthesis was not explicitly stated.
Well, you didn't mention efficiency so I'm going to post a really brute force solution and let you optimize it if you want to. Since you can have parantheses, it's easy to brute force it using Reverse Polish Notation:
First of all, if your set has n numbers, you must use exactly n - 1 operators. So your solution will be given by a sequence of 2n - 1 symbols from {{your given set}, {*, /, +, -}}
st = a stack of length 2n - 1
n = numbers in your set
a = your set, to which you add *, /, +, -
v[i] = 1 if the NUMBER i has been used before, 0 otherwise
void go(int k)
{
if ( k > 2n - 1 )
{
// eval st as described on Wikipedia.
// Careful though, it might not be valid, so you'll have to check that it is
// if it evals to your target value great, you can build your target from the given numbers. Otherwise, go on.
return;
}
for ( each symbol x in a )
if ( x isn't a number or x is a number but v[x] isn't 1 )
{
st[k] = x;
if ( x is a number )
v[x] = 1;
go(k + 1);
}
}
Generally speaking, when you need to do something recursively it helps to start from the "bottom" and think your way up.
Consider: You have a set S of n numbers {a,b,c,...}, and a set of four operations {+,-,*,/}. Let's call your recursive function that operates on the set F(S)
If n is 1, then F(S) will just be that number.
If n is 2, F(S) can be eight things:
pick your left-hand number from S (2 choices)
then pick an operation to apply (4 choices)
your right-hand number will be whatever is left in the set
Now, you can generalize from the n=2 case:
Pick a number x from S to be the left-hand operand (n choices)
Pick an operation to apply
your right hand number will be F(S-x)
I'll let you take it from here. :)
edit: Mark poses a valid criticism; the above method won't get absolutely everything. To fix that problem, you need to think about it in a slightly different way:
At each step, you first pick an operation (4 choices), and then
partition S into two sets, for the left and right hand operands,
and recursively apply F to both partitions
Finding all partitions of a set into 2 parts isn't trivial itself, though.
Your best clue about how to approach this problem is the fact that your teacher/professor wants you to use recursion. That is, this isn't a math problem - it is a search problem.
Not to give too much away (it is homework after all), but you have to spawn a call to the recursive function using an operator, a number and a list containing the remaining numbers. The recursive function will extract a number from the list and, using the operation passed in, combine it with the number passed in (which is your running total). Take the running total and call yourself again with the remaining items on the list (you'll have to iterate the list within the call but the sequence of calls is depth-first). Do this once for each of the four operators unless Success has been achieved by a previous leg of the search.
I updated this to use a list instead of a stack
When the result of the operation is your target number and your list is empty, then you have successfully found the set of operations (those that traced the path to the successful leaf) - set the Success flag and unwind. Note that the operators aren't on a list nor are they in the call: the function itself always iterates over all four. Your mechanism for "unwinding" the operator sequence from the successful leaf to get the sequence is to return the current operator and number prepended to the value returned by recursive call (only one of which will be successful since you stop at success - that, obviously, is the one to use). If none are successful, then what you return isn't important anyhow.
Update This is much harder when you have to consider expressions like the one that Daniel posted. You have combinatorics on the numbers and the groupings (numbers due to the fact that / and - are order sensitive even without grouping and grouping because it changes precedence). Then, of course, you also have the combinatorics of the operations. It is harder to manage the differences between (4 + 3) * 2 and 4 + (3 * 2) because grouping doesn't recurse like operators or numbers (which you can just iterate over in a breadth-first manner while making your (depth-first) recursive calls).
Here's some Python code to get you started: it just prints all the possible expressions, without worrying too much about redundancy. You'd need to modify it to evaluate expressions and compare to the target number, rather than simply printing them.
The basic idea is: given a set S of numbers, partition S into two subsets left and right in all possible ways (where we don't care about the order or the elements in left and right), such that left and right are both nonempty. Now for each of these partitions, find all ways of combining the elements in left (recursively!), and similarly for right, and combine the two resulting values with all possible operators. The recursion bottoms out when a set has just one element, in which case there's only one value possible.
Even if you don't know Python, the expressions function should be reasonably easy to follow; the splittings function contains some Python oddities, but all it does is to find all the partitions of the list l into left and right pieces.
def splittings(l):
n = len(l)
for i in xrange(2**n):
left = [e for b, e in enumerate(l) if i & 2**b]
right = [e for b, e in enumerate(l) if not i & 2**b]
yield left, right
def expressions(l):
if len(l) == 1:
yield l[0]
else:
for left, right in splittings(l):
if not left or not right:
continue
for el in expressions(left):
for er in expressions(right):
for operator in '+-*/':
yield '(' + el + operator + er + ')'
for x in expressions('1234'):
print x
pusedo code:
Works(list, target)
for n in list
tmp=list.remove(n)
return Works(tmp,target+n) or Works(tmp,target-n) or Works(tmp, n-target) or ...
then you just have to put the base case in. I think I gave away to much.