See also in Wikipedia and Rosetta code.

Layman's explanation in Russian: https://lenta.ru/articles/2012/10/15/nobel/.

My solution is much less efficient, because much simpler/better algorithm exists (Gale/Shapley algorithm), but I did it to demonstrate the essence of the problem plus as a yet another SMT-solvers and Z3 demonstration.

See comments:

#!/usr/bin/env python from z3 import * SIZE=10 # names and preferences has been copypasted from https://rosettacode.org/wiki/Stable_marriage_problem # males: abe, bob, col, dan, ed, fred, gav, hal, ian, jon = 0,1,2,3,4,5,6,7,8,9 MenStr=["abe", "bob", "col", "dan", "ed", "fred", "gav", "hal", "ian", "jon"] # females: abi, bea, cath, dee, eve, fay, gay, hope, ivy, jan = 0,1,2,3,4,5,6,7,8,9 WomenStr=["abi", "bea", "cath", "dee", "eve", "fay", "gay", "hope", "ivy", "jan"] # men's preferences. better is at left (at first): ManPrefer={} ManPrefer[abe]=[abi, eve, cath, ivy, jan, dee, fay, bea, hope, gay] ManPrefer[bob]=[cath, hope, abi, dee, eve, fay, bea, jan, ivy, gay] ManPrefer[col]=[hope, eve, abi, dee, bea, fay, ivy, gay, cath, jan] ManPrefer[dan]=[ivy, fay, dee, gay, hope, eve, jan, bea, cath, abi] ManPrefer[ed]=[jan, dee, bea, cath, fay, eve, abi, ivy, hope, gay] ManPrefer[fred]=[bea, abi, dee, gay, eve, ivy, cath, jan, hope, fay] ManPrefer[gav]=[gay, eve, ivy, bea, cath, abi, dee, hope, jan, fay] ManPrefer[hal]=[abi, eve, hope, fay, ivy, cath, jan, bea, gay, dee] ManPrefer[ian]=[hope, cath, dee, gay, bea, abi, fay, ivy, jan, eve] ManPrefer[jon]=[abi, fay, jan, gay, eve, bea, dee, cath, ivy, hope] # women's preferences: WomanPrefer={} WomanPrefer[abi]=[bob, fred, jon, gav, ian, abe, dan, ed, col, hal] WomanPrefer[bea]=[bob, abe, col, fred, gav, dan, ian, ed, jon, hal] WomanPrefer[cath]=[fred, bob, ed, gav, hal, col, ian, abe, dan, jon] WomanPrefer[dee]=[fred, jon, col, abe, ian, hal, gav, dan, bob, ed] WomanPrefer[eve]=[jon, hal, fred, dan, abe, gav, col, ed, ian, bob] WomanPrefer[fay]=[bob, abe, ed, ian, jon, dan, fred, gav, col, hal] WomanPrefer[gay]=[jon, gav, hal, fred, bob, abe, col, ed, dan, ian] WomanPrefer[hope]=[gav, jon, bob, abe, ian, dan, hal, ed, col, fred] WomanPrefer[ivy]=[ian, col, hal, gav, fred, bob, abe, ed, jon, dan] WomanPrefer[jan]=[ed, hal, gav, abe, bob, jon, col, ian, fred, dan] s=Solver() ManChoice=[Int('ManChoice_%d' % i) for i in range(SIZE)] WomanChoice=[Int('WomanChoice_%d' % i) for i in range(SIZE)] # all values in ManChoice[]/WomanChoice[] are in 0..9 range: for i in range(SIZE): s.add(And(ManChoice[i]>=0, ManChoice[i]<=9)) s.add(And(WomanChoice[i]>=0, WomanChoice[i]<=9)) s.add(Distinct(ManChoice)) # "inverted index", make sure all men and women are "connected" to each other, i.e., form pairs. # FIXME: only work for SIZE=10 for i in range(SIZE): s.add(WomanChoice[i]== If(ManChoice[0]==i, 0, If(ManChoice[1]==i, 1, If(ManChoice[2]==i, 2, If(ManChoice[3]==i, 3, If(ManChoice[4]==i, 4, If(ManChoice[5]==i, 5, If(ManChoice[6]==i, 6, If(ManChoice[7]==i, 7, If(ManChoice[8]==i, 8, If(ManChoice[9]==i, 9, -1))))))))))) # this is like ManChoice[] value, but "inverted index". it reflects wife's rating in man's own rating system. # 0 if he married best women, 1 if there is 1 women who he would prefer (if there is a chance): ManChoiceInOwnRating=[Int('ManChoiceInOwnRating_%d' % i) for i in range(SIZE)] # same for all women: WomanChoiceInOwnRating=[Int('WomanChoiceInOwnRating_%d' % i) for i in range(SIZE)] # set values in "inverted" indices according to values in ManPrefer[]/WomenPrefer[]. # FIXME: only work for SIZE=10 for m in range(SIZE): s.add (ManChoiceInOwnRating[m]== If(ManChoice[m]==ManPrefer[m][0],0, If(ManChoice[m]==ManPrefer[m][1],1, If(ManChoice[m]==ManPrefer[m][2],2, If(ManChoice[m]==ManPrefer[m][3],3, If(ManChoice[m]==ManPrefer[m][4],4, If(ManChoice[m]==ManPrefer[m][5],5, If(ManChoice[m]==ManPrefer[m][6],6, If(ManChoice[m]==ManPrefer[m][7],7, If(ManChoice[m]==ManPrefer[m][8],8, If(ManChoice[m]==ManPrefer[m][9],9, -1))))))))))) for w in range(SIZE): s.add (WomanChoiceInOwnRating[w]== If(WomanChoice[w]==WomanPrefer[w][0],0, If(WomanChoice[w]==WomanPrefer[w][1],1, If(WomanChoice[w]==WomanPrefer[w][2],2, If(WomanChoice[w]==WomanPrefer[w][3],3, If(WomanChoice[w]==WomanPrefer[w][4],4, If(WomanChoice[w]==WomanPrefer[w][5],5, If(WomanChoice[w]==WomanPrefer[w][6],6, If(WomanChoice[w]==WomanPrefer[w][7],7, If(WomanChoice[w]==WomanPrefer[w][8],8, If(WomanChoice[w]==WomanPrefer[w][9],9, -1))))))))))) # the last part is the essence of this script: # this is 2D bool array. "true" if a (married or already connected) man would prefer another women over his wife. ManWouldPrefer=[[Bool('ManWouldPrefer_%d_%d' % (m, w)) for w in range(SIZE)] for m in range(SIZE)] # same for all women: WomanWouldPrefer=[[Bool('WomanWouldPrefer_%d_%d' % (w, m)) for m in range(SIZE)] for w in range(SIZE)] # set "true" in ManWouldPrefer[][] table for all women who are better than the wife a man currently has. # all others can be "false" # if the man married best women, all entries would be "false" for m in range(SIZE): for w in range(SIZE): s.add(ManWouldPrefer[m][w] == (ManPrefer[m].index(w) < ManChoiceInOwnRating[m])) # do the same for WomanWouldPrefer[][]: for w in range(SIZE): for m in range(SIZE): s.add(WomanWouldPrefer[w][m] == (WomanPrefer[w].index(m) < WomanChoiceInOwnRating[w])) # this is the most important constraint. # enumerate all possible man/woman pairs # no pair can exist with both "true" in "mirrored" entries of ManWouldPrefer[][]/WomanWouldPrefer[][]. # we block this by the following constraint: Not(And(x,y)): all x/y values are allowed, except if both are set to 1/true: for m in range(SIZE): for w in range(SIZE): s.add(Not(And(ManWouldPrefer[m][w], WomanWouldPrefer[w][m]))) print s.check() mdl=s.model() print "" print "ManChoice:" for m in range(SIZE): w=mdl[ManChoice[m]].as_long() print MenStr[m], "<->", WomenStr[w] print "" print "WomanChoice:" for w in range(SIZE): m=mdl[WomanChoice[w]].as_long() print WomenStr[w], "<->", MenStr[m]

( The source code: https://github.com/DennisYurichev/yurichev.com/blob/master/blog/stable_marriage/stable.py )

Result is seems to be correct:

sat ManChoice: abe <-> ivy bob <-> cath col <-> dee dan <-> fay ed <-> jan fred <-> bea gav <-> gay hal <-> eve ian <-> hope jon <-> abi WomanChoice: abi <-> jon bea <-> fred cath <-> bob dee <-> col eve <-> hal fay <-> dan gay <-> gav hope <-> ian ivy <-> abe jan <-> ed

This is what we did in plain English language. "Connect men and women somehow, we don't care how. But no pair must exist of those who prefer each other (simultaneously) over their current spouses". Gale/Shapley algorithm uses "steps" to "stabilize" marriage. There are no "steps", all pairs are married couples already.

Another important thing to notice: only one solution must exist.

... results=[] # enumerate all possible solutions: while True: if s.check() == sat: m = s.model() #print m results.append(m) block = [] for d in m: c=d() block.append(c != m[d]) s.add(Or(block)) else: print "results total=", len(results) break ...

( The source code: https://github.com/DennisYurichev/yurichev.com/blob/master/blog/stable_marriage/stable2.py )

That reports only 1 model available, which is correct indeed.

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