More Like this

1. On the hardness of dominant strategy mechanism design

   https://doi.org/10.1145/3519935.3520013  

   Dobzinski, Shahar; Ron, Shiri; Vondrák, Jan (June 2022, STOC 2022: Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing)

   Stefano Leonardi (Ed.)

   We study the communication complexity of dominant strategy implementations of combinatorial auctions. We start with two domains that are generally considered “easy”: multi-unit auctions with decreasing marginal values and combinatorial auctions with gross substitutes valuations. For both domains we have fast algorithms that find the welfare-maximizing allocation with communication complexity that is poly-logarithmic in the input size. This immediately implies that welfare maximization can be achieved in ex-post equilibrium with no significant communication cost, by using VCG payments. In contrast, we show that in both domains the communication complexity of any dominant strategy implementation that achieves the optimal welfare is polynomial in the input size. We then move on to studying the approximation ratios achievable by dominant strategy mechanisms. For multi-unit auctions with decreasing marginal values, we provide a dominant-strategy communication FPTAS. For combinatorial auctions with general valuations, we show that there is no dominant strategy mechanism that achieves an approximation ratio better than m1−є that uses poly(m,n) bits of communication, where m is the number of items and n is the number of bidders. In contrast, a randomized dominant strategy mechanism that achieves an O(√m) approximation with poly(m,n) communication is known. This proves the first gap between computationally efficient deterministic dominant strategy mechanisms and randomized ones. En route, we answer an open question on the communication cost of implementing dominant strategy mechanisms for more than two players, and also solve some open problems in the area of simultaneous combinatorial auctions. 

   more » « less
2. Approximating nash social welfare under rado valuations

   https://doi.org/10.1145/3476436.3476444  

   Garg, Jugal; Husić, Edin; Végh, László A. (June 2021, ACM SIGecom Exchanges)

   The Nash social welfare problem asks for an allocation of indivisible items to agents in order to maximize the geometric mean of agents' valuations. We give an overview of the constant-factor approximation algorithm for the problem when agents have Rado valuations [Garg et al. 2021]. Rado valuations are a common generalization of the assignment (OXS) valuations and weighted matroid rank functions. Our approach also gives the first constant-factor approximation algorithm for the asymmetric Nash social welfare problem under the same valuations, provided that the maximum ratio between the weights is bounded by a constant. 

   more » « less
3. Zarankiewicz’s problem for semilinear hypergraphs

   https://doi.org/10.1017/fms.2021.52  

   Basit, Abdul; Chernikov, Artem; Starchenko, Sergei; Tao, Terence; Tran, Chieu-Minh (January 2021, Forum of Mathematics, Sigma)

   Abstract A bipartite graph $$H = \left (V_1, V_2; E \right )$$ with $$\lvert V_1\rvert + \lvert V_2\rvert = n$$ is semilinear if $$V_i \subseteq \mathbb {R}^{d_i}$$ for some $$d_i$$ and the edge relation E consists of the pairs of points $$(x_1, x_2) \in V_1 \times V_2$$ satisfying a fixed Boolean combination of s linear equalities and inequalities in $$d_1 + d_2$$ variables for some s . We show that for a fixed k , the number of edges in a $$K_{k,k}$$ -free semilinear H is almost linear in n , namely $$\lvert E\rvert = O_{s,k,\varepsilon }\left (n^{1+\varepsilon }\right )$$ for any $$\varepsilon> 0$$ ; and more generally, $$\lvert E\rvert = O_{s,k,r,\varepsilon }\left (n^{r-1 + \varepsilon }\right )$$ for a $$K_{k, \dotsc ,k}$$ -free semilinear r -partite r -uniform hypergraph. As an application, we obtain the following incidence bound: given $$n_1$$ points and $$n_2$$ open boxes with axis-parallel sides in $$\mathbb {R}^d$$ such that their incidence graph is $$K_{k,k}$$ -free, there can be at most $$O_{k,\varepsilon }\left (n^{1+\varepsilon }\right )$$ incidences. The same bound holds if instead of boxes, one takes polytopes cut out by the translates of an arbitrary fixed finite set of half-spaces. We also obtain matching upper and (superlinear) lower bounds in the case of dyadic boxes on the plane, and point out some connections to the model-theoretic trichotomy in o -minimal structures (showing that the failure of an almost-linear bound for some definable graph allows one to recover the field operations from that graph in a definable manner). 

   more » « less
4. A deterministic near-linear time approximation scheme for geometric transportation

   https://doi.org/10.1109/FOCS57990.2023.00078  

   Fox, Emily; Lu, Jiashuai (November 2023, IEEE)

   Given a set of points $$P = (P^+ \sqcup P^-) \subset \mathbb{R}^d$$ for some constant $$d$$ and a supply function $$\mu:P\to \mathbb{R}$$ such that $$\mu(p) > 0~\forall p \in P^+$$, $$\mu(p) < 0~\forall p \in P^-$$, and $$\sum_{p\in P}{\mu(p)} = 0$$, the geometric transportation problem asks one to find a transportation map $$\tau: P^+\times P^-\to \mathbb{R}_{\ge 0}$$ such that $$\sum_{q\in P^-}{\tau(p, q)} = \mu(p)~\forall p \in P^+$$, $$\sum_{p\in P^+}{\tau(p, q)} = -\mu(q) \forall q \in P^-$$, and the weighted sum of Euclidean distances for the pairs $$\sum_{(p,q)\in P^+\times P^-}\tau(p, q)\cdot ||q-p||_2$$ is minimized. We present the first deterministic algorithm that computes, in near-linear time, a transportation map whose cost is within a $$(1 + \varepsilon)$$ factor of optimal. More precisely, our algorithm runs in $$O(n\varepsilon^{-(d+2)}\log^5{n}\log{\log{n}})$$ time for any constant $$\varepsilon > 0$$. While a randomized $$n\varepsilon^{-O(d)}\log^{O(d)}{n}$$ time algorithm for this problem was discovered in the last few years, all previously known deterministic $$(1 + \varepsilon)$$-approximation algorithms run in~$$\Omega(n^{3/2})$$ time. A similar situation existed for geometric bipartite matching, the special case of geometric transportation where all supplies are unit, until a deterministic $$n\varepsilon^{-O(d)}\log^{O(d)}{n}$$ time $$(1 + \varepsilon)$$-approximation algorithm was presented at STOC 2022. Surprisingly, our result is not only a generalization of the bipartite matching one to arbitrary instances of geometric transportation, but it also reduces the running time for all previously known $$(1 + \varepsilon)$$-approximation algorithms, randomized or deterministic, even for geometric bipartite matching. In particular, we give the first $$(1 + \varepsilon)$$-approximate deterministic algorithm for geometric bipartite matching and the first $$(1 + \varepsilon)$$-approximate deterministic or randomized algorithm for geometric transportation with no dependence on $$d$$ in the exponent of the running time's polylog. As an additional application of our main ideas, we also give the first randomized near-linear $$O(\varepsilon^{-2} m \log^{O(1)} n)$$ time $$(1 + \varepsilon)$$-approximation algorithm for the uncapacitated minimum cost flow (transshipment) problem in undirected graphs with arbitrary \emph{real} edge costs. 

   more » « less
5. Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications

   https://doi.org/10.2478/popets-2022-0020  

   Lu, Donghang; Yu, Albert; Kate, Aniket; Maji, Hemanta (November 2021, Proceedings on Privacy Enhancing Technologies)

   Abstract While the practicality of secure multi-party computation (MPC) has been extensively analyzed and improved over the past decade, we are hitting the limits of efficiency with the traditional approaches of representing the computed functionalities as generic arithmetic or Boolean circuits. This work follows the design principle of identifying and constructing fast and provably-secure MPC protocols to evaluate useful high-level algebraic abstractions; thus, improving the efficiency of all applications relying on them. We present Polymath, a constant-round secure computation protocol suite for the secure evaluation of (multi-variate) polynomials of scalars and matrices, functionalities essential to numerous data-processing applications. Using precise natural precomputation and high-degree of parallelism prevalent in the modern computing environments, Polymath can make latency of secure polynomial evaluations of scalars and matrices independent of polynomial degree and matrix dimensions. We implement our protocols over the HoneyBadgerMPC library and apply it to two prominent secure computation tasks: privacy-preserving evaluation of decision trees and privacy-preserving evaluation of Markov processes. For the decision tree evaluation problem, we demonstrate the feasibility of evaluating high-depth decision tree models in a general n -party setting. For the Markov process application, we demonstrate that Poly-math can compute large powers of transition matrices with better online time and less communication. 

   more » « less