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Proof step 3 from proof replay-log-append-one (previous step)

⊦ lpt.mapIndices(elems nx ++ n, λ lp. mklp(lp.ref-size, LEB_SIZE, LP_GROUP_NODES, lp.gcheapidx)) = lpt.mapIndices(elems nx, λ lp. mklp(lp.ref-size, LEB_SIZE, LP_GROUP_NODES, lp.gcheapidx)) [n, mklp(lpt[n].ref-size, LEB_SIZE, LP_GROUP_NODES, lpt[n].gcheapidx) ], n ∈ nx

Heuristic elimination applied rule insert elim lemma

And obtained the following new goals

Proof step 4:
lpt[n] = mklp(m, m0, lpf, m1) ⊦ lpt.mapIndices(elems nx ++ n, λ lp. mklp(lp.ref-size, LEB_SIZE, LP_GROUP_NODES, lp.gcheapidx)) = lpt.mapIndices(elems nx, λ lp. mklp(lp.ref-size, LEB_SIZE, LP_GROUP_NODES, lp.gcheapidx))[n, mklp(m, LEB_SIZE, LP_GROUP_NODES, m1)], n ∈ nx

Rule argument

spec: lprops name: elim seq: ⊦ ((m = lp.ref-size ∧ m0 = lp.size) ∧ lpf = lp.flags) ∧ m1 = lp.gcheapidx ↔ lp = mklp(m, m0, lpf, m1) subst: [lp, m, m0, lpf, m1] → [lpt[n], m, m0, lpf, m1]

Additional information

Used simplifier rules: ⊦ ((m = lp.ref-size ∧ m0 = lp.size) ∧ lpf = lp.flags) ∧ m1 = lp.gcheapidx ↔ lp = mklp(m, m0, lpf, m1); (elim) Used terms: lpt[n], m, m0, lpf, m1