In this paper we apply the instrumental approach to social preferences in order to distinguish among various shapes of preferences for social status. In particular, we consider the shape of reduced preferences that emerge in the equilibrium of a twosided matching model with non-transferable utility. Cole et al. (1992, 1995) show that, under full observability of potential mates’ attributes, ins...

As distributed systems increase in size, the need for scalable algorithms becomes more and more important. We argue that in the context of system identification, an essential building block of any scalable algorithm is the ability to estimate local dynamics within a large interconnected system. We show that in what we term the “full interconnection measurement” setting, this task is easily solv...

 Abstract— We characterize the total degrees of freedom (DoF) of the full-rank MIMO X channel with arbitrary number of antennas at each node. We elucidate that the existing outer bound is tight for any antenna configuration and provide transmit and receive filter designs that attain this outer bound. The proposed achievable scheme exploits channel extensions in terms of both, symbol and asymme...

(c) Columns of A are independent. (d) A is tall (i.e., n ≤ m) and full-rank (i.e., rank(A) = min(m,n) = n). Solution: We will show the chain of equivalences (a) =⇒ (b) =⇒ (c) =⇒ (d) =⇒ (a). (a) =⇒ (b): By the rank–nullity theorem, we have dim(N (A)) + rank(A) = n, which implies rank(A) = n (since dim(N (A)) = 0). Since rank(A) = rank(A ), we then have rank(A ) = n. Since rank is equivalent to t...

In this paper, we propose three new tensor decompositions for even-order tensors corresponding respectively to the rank-one decompositions of some unfolded matrices. Consequently such new decompositions lead to three new notions of (even-order) tensor ranks, to be called the M-rank, the symmetric M-rank, and the strongly symmetric M-rank in this paper. We discuss the bounds between these new te...

In this paper, we study low-rank matrix nearness problems, with a focus on learning lowrank positive semidefinite (kernel) matrices for machine learning applications. We propose efficient algorithms that scale linearly in the number of data points and quadratically in the rank of the input matrix. Existing algorithms for learning kernel matrices often scale poorly, with running times that are c...

This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive redu...

The design of space–time codes to achieve full spatial diversity over fading channels has largely been addressed by handcrafting example codes using computer search methods and only for small numbers of antennas. The lack of more general designs is in part due to the fact that the diversity advantage of a code is the minimum rank among the complex baseband differences between modulated codeword...

An isomorphism of symplectically tame smooth pseudocomplex structures on the complex projective plane which is a homeomorphism and differentiable of full rank at two points is smooth.