In 1991, Craig Gotsman and Nathan Linial conjectured that for all $n$ and $d$, the average sensitivity of a degree-$d$ polynomial threshold function on $n$ variables is maximized by the degree-$d$ symmetric polynomial which computes the parity function on the $d$ layers of the hypercube with Hamming weight closest to $n/2$. We refute the conjecture for almost all $d$ and for almost all $n$, and we confirm the conjecture in many of the remaining cases.
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We study the problem of the nonparametric estimation for the density $\pi$ of the stationary distribution of a $d$-dimensional stochastic differential equation $(X_t)_{t \in [0, T]}$. From the continuous observation of the sampling path on $[0, T]$, we study the rate of estimation of $\pi(x)$ as $T$ goes to infinity. One finding is that, for $d \ge 3$, the rate of estimation depends on the smoothness $\beta = (\beta_1, ... , \beta_d)$ of $\pi$. In particular, having ordered the smoothness such that $\beta_1 \le ... \le \beta_d$, it depends on the fact that $\beta_2 < \beta_3$ or $\beta_2 = \beta_3$. We show that kernel density estimators achieve the rate $(\frac{\log T}{T})^\gamma$ in the first case and $(\frac{1}{T})^\gamma$ in the second, for an explicit exponent $\gamma$ depending on the dimension and on $\bar{\beta}_3$, the harmonic mean of the smoothness over the $d$ directions after having removed $\beta_1$ and $\beta_2$, the smallest ones. Moreover, we obtain a minimax lower bound on the $\mathbf{L}^2$-risk for the pointwise estimation with the same rates $(\frac{\log T}{T})^\gamma$ or $(\frac{1}{T})^\gamma$, depending on the value of $\beta_2$ and $\beta_3$.
A central problem in proof-theory is that of finding criteria for identity of proofs, that is, for when two distinct formal derivations can be taken as denoting the same logical argument. In the literature one finds criteria which are either based on proof normalization (two derivations denote the same proofs when they have the same normal form) or on the association of formal derivations with graph-theoretic structures (two derivations denote they same proof when they are associated with the same graph). In this paper we argue for a new criterion for identity of proofs which arises from the interpretation of formal rules and derivations as natural transformations of a suitable kind. We show that the naturality conditions arising from this interpretation capture in a uniform and elegant ways several forms of "rule-permutations" which are found in proof-systems for propositional, first- and second-order logic.
We prove new optimality results for adaptive mesh refinement algorithms for non-symmetric, indefinite, and time-dependent problems by proposing a generalization of quasi-orthogonality which follows directly from the inf-sup stability of the underlying problem. This completely removes a central technical difficulty in modern proofs of optimal convergence of adaptive mesh refinement algorithms and leads to simple optimality proofs for the Taylor-Hood discretization of the stationary Stokes problem, a finite-element/boundary-element discretization of an unbounded transmission problem, and an adaptive time-stepping scheme for parabolic equations. The main technical tool are new stability bounds for the $LU$-factorization of matrices together with a recently established connection between quasi-orthogonality and matrix factorization.
The Super-SAT or SSAT problem was introduced by Dinur et al.(2002,2003) to prove the NP-hardness of approximation of two popular lattice problems - Shortest Vector Problem(SVP) and Closest Vector Problem(CVP). They conjectured that SSAT is NP-hard to approximate to within a factor of $n^c$ ($c>0$ is constant), where $n$ is the size of the SSAT instance. In this paper we prove this conjecture assuming the Projection Games Conjecture(PGC), given by Moshkovitz (2012). This implies hardness of approximation of SVP and CVP within polynomial factors, assuming PGC. We also reduce SSAT to the Nearest Codeword Problem(NCP) and Learning Halfspace Problem(LHP), as considered by Arora et al.(1997). This proves that both these problems are NP-hard to approximate within a factor of $N^{c'/\log\log n}$($c'>0$ is constant) where $N$ is the size of the instances of the respective problems. Assuming PGC these problems are proved to be NP-hard to approximate within polynomial factors.
In this paper, we mainly study the gradient based Jacobi-type algorithms to maximize two classes of homogeneous polynomials with orthogonality constraints, and establish their convergence properties. For the first class of homogeneous polynomials subject to a constraint on a Stiefel manifold, we reformulate it as an optimization problem on a unitary group, which makes it possible to apply the gradient based Jacobi-type (Jacobi-G) algorithm. Then, if the subproblem can be always represented as a quadratic form, we establish the global convergence of Jacobi-G under any one of the three conditions (A1), (A2) and (A3). The convergence result for (A1) is an easy extension of the result in [Usevich et al. SIOPT 2020], while (A2) and (A3) are two new ones. This algorithm and the convergence properties apply to the well-known joint approximate symmetric tensor diagonalization and joint approximate symmetric trace maximization. For the second class of homogeneous polynomials subject to constraints on the product of Stiefel manifolds, we similarly reformulate it as an optimization problem on the product of unitary groups, and then develop a new gradient based multi-block Jacobi-type (Jacobi-MG) algorithm to solve it. We similarly establish the global convergence of Jacobi-MG under any one of the three conditions (A1), (A2) and (A3), if the subproblem can be always represented as a quadratic form. This algorithm and the convergence properties apply to the well-known joint approximate tensor diagonalization and joint approximate tensor compression. As the proximal variants of Jacobi-G and Jacobi-MG, we also propose the Jacobi-GP and Jacobi-MGP algorithms, and establish their global convergence without any further condition. Some numerical results are provided indicating the efficiency of the proposed algorithms.
Let a polytope $\mathcal{P}$ be defined by one of the following ways: (i) $\mathcal{P} = \{x \in \mathbb{R}^n \colon A x \leq b\}$, where $A \in \mathbb{Z}^{(n+m) \times n}$, $b \in \mathbb{Z}^{(n+m)}$, and $rank(A) = n$, (ii) $\mathcal{P} = \{x \in \mathbb{R}_+^n \colon A x = b\}$, where $A \in \mathbb{Z}^{m \times n}$, $b \in \mathbb{Z}^{m}$, and $rank(A) = m$, and let all the rank minors of $A$ be bounded by $\Delta$ in the absolute values. We show that $|\mathcal{P} \cap \mathbb{Z}^n|$ can be computed with an algorithm, having the arithmetic complexity bound $$ O\bigl(d^{m + 4} \cdot \Delta^4 \cdot \log(\Delta) \bigr), $$ where $d = \dim(\mathcal{P})$, which outperforms the previous best known complexity bound $O(d^{m + O(1)} \cdot d^{\log_2(\Delta)})$. We do not directly compute the short rational generating function for $\mathcal{P} \cap \mathbb{Z}^n$, but compute its particular representation in the form of exponential series that depends on only one variable. The parametric versions of the above problem are also considered.
We prove discrete Helly-type theorems for pseudohalfplanes, which extend recent results of Jensen, Joshi and Ray about halfplanes. Among others we show that given a family of pseudohalfplanes $\cal H$ and a set of points $P$, if every triple of pseudohalfplanes has a common point in $P$ then there exists a set of at most two points that hits every pseudohalfplane of $\cal H$. We also prove that if every triple of points of $P$ is contained in a pseudohalfplane of $\cal H$ then there are two pseudohalfplanes of $\cal H$ that cover all points of $P$. To prove our results we regard pseudohalfplane hypergraphs, define their extremal vertices and show that these behave in many ways as points on the boundary of the convex hull of a set of points. Our methods are purely combinatorial. In addition we determine the maximal possible chromatic number of the regarded hypergraph families.
We prove a complexity dichotomy for a class of counting problems expressible as bipartite 3-regular Holant problems. For every problem of the form $\operatorname{Holant}\left(f\mid =_3 \right)$, where $f$ is any integer-valued ternary symmetric constraint function on Boolean variables, we prove that it is either P-time computable or #P-hard, depending on an explicit criterion of $f$. The constraint function can take both positive and negative values, allowing for cancellations. The dichotomy extends easily to rational valued functions of the same type. In addition, we discover a new phenomenon: there is a set $\mathcal{F}$ with the property that for every $f \in \mathcal{F}$ the problem $\operatorname{Holant}\left(f\mid =_3 \right)$ is planar P-time computable but #P-hard in general, yet its planar tractability is by a combination of a holographic transformation by $\left[\begin{smallmatrix} 1 & 1 \\ 1 & -1 \end{smallmatrix}\right]$ to FKT together with an independent global argument.
We provide a sufficient condition for solvability of a system of real quadratic equations $p_i(x)=y_i$, $i=1, \ldots, m$, where $p_i: {\mathbb R}^n \longrightarrow {\mathbb R}$ are quadratic forms. By solving a positive semidefinite program, one can reduce it to another system of the type $q_i(x)=\alpha_i$, $i=1, \ldots, m$, where $q_i: {\mathbb R}^n \longrightarrow {\mathbb R}$ are quadratic forms and $\alpha_i=\mathrm{tr\ } q_i$. We prove that the latter system has solution $x \in {\mathbb R}^n$ if for some (equivalently, for any) orthonormal basis $A_1,\ldots, A_m$ in the space spanned by the matrices of the forms $q_i$, the operator norm of $A_1^2 + \ldots + A_m^2$ does not exceed $\eta/m$ for some absolute constant $\eta > 0$. The condition can be checked in polynomial time and is satisfied, for example, for random $q_i$ provided $m \leq \gamma \sqrt{n}$ for an absolute constant $\gamma >0$. We prove a similar sufficient condition for a system of homogeneous quadratic equations to have a non-trivial solution. While the condition we obtain is of an algebraic nature, the proof relies on analytic tools including Fourier analysis and measure concentration.
We study the problem of maximizing the probability that (i) an electric component or financial institution $X$ does not default before another component or institution $Y$ and (ii) that $X$ and $Y$ default jointly within the class of all random variables $X,Y$ with given univariate continuous distribution functions $F$ and $G$, respectively, and show that the maximization problems correspond to finding copulas maximizing the mass of the endograph $\Gamma^\leq(T)$ and the graph $\Gamma(T)$ of $T=G \circ F^-$, respectively. After providing simple, copula-based proofs for the existence of copulas attaining the two maxima $\overline{m}_T$ and $\overline{w}_T$ we generalize the obtained results to the case of general (not necessarily monotonic) transformations $T:[0,1] \rightarrow [0,1]$ and derive simple and easily calculable formulas for $\overline{m}_T$ and $\overline{w}_T$ involving the distribution function $F_T$ of $T$ (interpreted as random variable on $[0,1]$). The latter are then used to charac\-terize all non-decreasing transformations $T:[0,1] \rightarrow [0,1]$ for which $\overline{m}_T$ and $\overline{w}_T$ coincide. A strongly consistent estimator for the maximum probability that $X$ does not default before $Y$ is derived and proven to be asymptotically normal under very mild regularity conditions. Several examples and graphics illustrate the main results and falsify some seemingly natural conjectures.
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