No way thru maths, some ways thru apps!
As many have posted so far, it is just as simple as this:
"The number of your 'independent' equations should equal to the number of your unknowns, to have a finite solution."
BUT,
This is only valid for a finite solution requirement. That is your relation should be one-to-one. However, if you are OK with picking up any of the infinite solutions, then what you should do is to randomly pick numbers as many as your lacking equations (#unknowns - #equations), then assign them to your unknowns ([#unknowns - #equations] of those), and solve the equations. More openly;
a-b = w .....(1) 4 eqns, 7 unknowns (a,b,c,d,w,x,y)
c-w = x .....(2) #eqn - #unknown = 4-7 = 3
d-x = y .....(3) Make up 3 values; 1, 5, 7
9-y = 2 .....(4) Assign them as; a=1,b=5,c=7
=>
y=9-2=7 ; w=a-b=1-5=-4 ; x=c-w=7-(-4)=11 d=y+x=7+11=18
so;
a=1 , b=5 , c=7 , d=18 , w=-4 , x=11 , y=7
See how it is solved? :)
By this method you can trial many combinations of these variables through a code. If you have an oppurtunity to set constraints for the range of these three variables, then you can identify the ranges of the solution set, as well. But, no unique (finite) solution will ever be possible.
That's my approach. I hope it helps..
I hate to rain on your parade
After Posting a reply, I realized that I have another thought on your problem.
From your later Posts, it looks as though you are trying to develop a compression algorithm.
If so, you should realize that excellent (and some not so good) minds have worked on compression algorithms for at least 40 years. If you want to come up with a better mousetrap, you should look at the plans for previous mousetraps to avoid reinventing old ideas. Einstein once said that a midget standing on the shoulders of a giant can see farther than the giant (he meant that he studied a lot of classical physics before developing his own theories). Perhaps a fresh mind looking at some old algorithms can see something that was overlooked.
A man named Shannon developed some important concepts in Information Theory. That area of knowledge indicates some limits on how good compression algorithms can be.