Buying to Bundle: Asymptotic Optimality of Surrogate BundlingResearch Paper
A platform sourcing items from monopolistic sellers with private quality cannot tractably maximize its true profit: the bundle revenue $Rev(v_S)$ is neither monotone, submodular, supermodular, subadditive, nor superadditive. Theorem 4.6 of *Buying to Bundle: Optimal Sourcing from Monopolistic Sellers* shows that the simple surrogate threshold mechanism — maximize the linearized objective $\varpi(x)=N\,E[x(\mu)(\mu-\varphi(\mu))]$ — is profit-optimal up to a $1+O(N^{-1/3})$ factor in large markets. Prove it: Bernoulli concentration for the bundle quality plus sub-exponential control of the dispersion gap $|Rev(v)-E[v]|$ (Lemma 4.5).
First raised for two variables by Ludwig Kraus in 1884 and stated in full generality by Ott-Heinrich Keller in 1939, the Jacobian conjecture asks something that sounds almost like freshman calculus: if a polynomial map from complex n-space to itself has a Jacobian determinant equal to a nonzero constant, must it be invertible by another polynomial map? That constant-Jacobian condition is precisely the algebraic shadow of the inverse function theorem, yet producing a polynomial — not merely analytic — inverse has resisted every attack for over eighty years. Shreeram Abhyankar championed the problem because it can be stated 'using little beyond a knowledge of calculus,' and Stephen Smale placed it sixteenth on his 1998 list of problems for the new century. Its notoriety is sharpened by a graveyard of published 'proofs' that later collapsed. Deep reductions exist — Bass, Connell, and Wright showed in 1982 that the general case reduces to maps of degree three — and the problem is equivalent, through work of Tsuchimoto, Belov-Kanel, and Kontsevich, to the Dixmier conjecture on the Weyl algebra. A formal statement anchors this famously slippery problem so that progress can be verified rather than merely believed.
Every time a streaming service guesses what you would rate a film you have never seen, it is solving a matrix completion problem: fill in the missing entries of a vast user-by-item table from the few that are observed. The question became famous during the Netflix Prize (2006-2009), and it looks hopeless - infinitely many matrices fit the observed entries - until one assumes the structure that makes recommendation possible: the table is essentially low rank, because tastes are governed by a few latent factors. In their landmark 2009 paper 'Exact Matrix Completion via Convex Optimization' (Foundations of Computational Mathematics), Emmanuel Candes and Benjamin Recht proved that an n-by-n matrix of rank r can be recovered exactly, with high probability, from only about n^1.2 * r * log n randomly observed entries - not by the NP-hard route of minimizing rank, but by minimizing the nuclear norm, a convex surrogate (the sum of the singular values) solvable efficiently. The proof, in the lineage of Candes-Romberg-Tao compressed sensing, turns on two ideas: an incoherence condition ensuring the singular vectors are spread out rather than spiky, and a dual certificate witnessing optimality, whose existence rests on delicate random-matrix concentration. It transformed a practical engineering puzzle into rigorous theory and seeded a decade of work across machine learning, signal processing, computer vision, and sensor localization. This mission formalizes the Candes-Recht exact-recovery theorem in Lean, decomposed into its dual-certificate construction and the probabilistic concentration reductions at its core.
A complete formal proof of Fermats Last Theorem for exponent 5: for all positive natural numbers a,b,c, a^5 + b^5 != c^5. The proof follows the classical Legendre-Dirichlet approach (1825-1830): Case 1 (5 does not divide a,b,c) is dispatched by congruences, and Case 2 (5 divides one of them) uses infinite descent through the ring Z[zeta_5]. The open hard leaf is the Z[zeta_5] PID step (flt5_zeta5_ring_witnesses).