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feat: build objective from factors dict in product or sum mode

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Pagwin 2026-06-11 19:28:20 -04:00
parent 43636d330e
commit 98eeb18e3c
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1 changed files with 17 additions and 20 deletions
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solve.py
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@ -1596,25 +1596,22 @@ def solve(
# ======================================================================
# OBJECTIVE IS SET HERE
# ======================================================================
def Eprod(v):
return v * v
def Bprod(v):
return v
def Sprod(v):
return v * v
prodEE = m.NewIntVar(0, Eprod(capE), "prodEE")
m.AddMultiplicationEquality(prodEE, [finalE, finalE])
prodSS = m.NewIntVar(0, Sprod(capS), "prodSS")
m.AddMultiplicationEquality(prodSS, [finalS, finalS])
prodBB = m.NewIntVar(0, Bprod(capB), "prodBB")
m.AddMultiplicationEquality(prodBB, [finalB])
prodEB = m.NewIntVar(0, Eprod(capE) * Bprod(capB), "prodEB")
m.AddMultiplicationEquality(prodEB, [prodEE, prodBB])
obj = m.NewIntVar(0, Eprod(capE) * Bprod(capB) * Sprod(capS), "obj")
m.AddMultiplicationEquality(obj, [prodEB, prodSS])
if objective_mode == "sum":
# Linear: CP-SAT takes weighted sums (negative weights included)
# directly, no auxiliary variables needed.
m.Maximize(sum(f * finals[k] for k, f in obj_factors.items() if f))
else:
# Product: maximize prod(finals[k] ** obj_factors[k]). Expand the
# exponents into a flat factor list and fold pairwise, carrying a
# running upper bound from the Phase-1 caps.
factor_keys = [k for k, f in obj_factors.items() for _ in range(f)]
obj = finals[factor_keys[0]]
bound = caps[factor_keys[0]]
for k in factor_keys[1:]:
bound *= caps[k]
nxt = m.NewIntVar(0, bound, "")
m.AddMultiplicationEquality(nxt, [obj, finals[k]])
obj = nxt
m.Maximize(obj)
# ---- Phase 2: solve the product to optimality ----