scholarly journals A Randomly Weighted Minimum Arborescence with a Random Cost Constraint

Author(s):  
Alan M. Frieze ◽  
Tomasz Tkocz

We study the minimum spanning arborescence problem on the complete digraph [Formula: see text], where an edge e has a weight We and a cost Ce, each of which is an independent uniform random variable Us, where [Formula: see text] and U is uniform [Formula: see text]. There is also a constraint that the spanning arborescence T must satisfy [Formula: see text]. We establish, for a range of values for [Formula: see text], the asymptotic value of the optimum weight via the consideration of a dual problem.

10.37236/9445 ◽  
2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Alan Frieze ◽  
Tomasz Tkocz

We study the minimum spanning tree problem on the complete graph $K_n$ where an edge $e$ has a weight $W_e$ and a cost $C_e$, each of which is an independent copy of the random variable $U^\gamma$ where $\gamma\leq 1$ and $U$ is  the uniform $[0,1]$ random variable. There is also a constraint that the spanning tree $T$ must satisfy $C(T)\leq c_0$. We establish, for a range of values for $c_0,\gamma$, the asymptotic value of the optimum weight via the consideration of a dual problem. 


2019 ◽  
Vol 7 ◽  
Author(s):  
ANIRBAN BASAK ◽  
ELLIOT PAQUETTE ◽  
OFER ZEITOUNI

We consider the spectrum of additive, polynomially vanishing random perturbations of deterministic matrices, as follows. Let$M_{N}$be a deterministic$N\times N$matrix, and let$G_{N}$be a complex Ginibre matrix. We consider the matrix${\mathcal{M}}_{N}=M_{N}+N^{-\unicode[STIX]{x1D6FE}}G_{N}$, where$\unicode[STIX]{x1D6FE}>1/2$. With$L_{N}$the empirical measure of eigenvalues of${\mathcal{M}}_{N}$, we provide a general deterministic equivalence theorem that ties$L_{N}$to the singular values of$z-M_{N}$, with$z\in \mathbb{C}$. We then compute the limit of$L_{N}$when$M_{N}$is an upper-triangular Toeplitz matrix of finite symbol: if$M_{N}=\sum _{i=0}^{\mathfrak{d}}a_{i}J^{i}$where$\mathfrak{d}$is fixed,$a_{i}\in \mathbb{C}$are deterministic scalars and$J$is the nilpotent matrix$J(i,j)=\mathbf{1}_{j=i+1}$, then$L_{N}$converges, as$N\rightarrow \infty$, to the law of$\sum _{i=0}^{\mathfrak{d}}a_{i}U^{i}$where$U$is a uniform random variable on the unit circle in the complex plane. We also consider the case of slowly varying diagonals (twisted Toeplitz matrices), and, when$\mathfrak{d}=1$, also of independent and identically distributed entries on the diagonals in$M_{N}$.


1948 ◽  
Vol 1 (01) ◽  
pp. 15-21
Author(s):  
Robert Watson-Watt

The objectives of navigation have, in the special case of air services, been summed up, in an ‘R.S.V.P.’ formula, as Regularity, Safety, Versatility and Punctuality. The formula holds for all navigation, since the object of every operator and every master is to carry his passenger or freight in safety on a preconceived time schedule, with virtually complete freedom of choice, by owner or master, of type of craft, of time of departure and of route. The enemies of this freedom of choice and of the preconceived schedule are almost wholly meteorological or astronomical; the only serious enemies are meteorological. The continuing concern of the ‘navigator’ proper is to avoid, detect and rectify departures from the preselected route and schedule. The whole task of ‘navigation’ is unfulfilled without an infallible look-out for dangerous obstacles, which may be at fixed and known absolute positions, or at random, variable and unknown positions relative to the craft. Good ‘eyes’ naturally or artificially aided and a good clock would be the only indispensable tools of whole navigation, were it not for those imperfections of the science and art of weather forecasting which leave the drift term in dead reckoning such a deplorably variable and unmanageable element in the dual problem of the navigator. That dual problem is ‘Where am I now?’ and ‘Where shall I be inxminutes from now?’ The unpredictability of the drift term and the imperfections of the navigator and his instruments prevent the answer to the second question emerging mechanically from the answers to the two much simpler and essentially similar questions: ‘Where am I now?’ and ‘Where was Iyminutes ago?’


2021 ◽  
Vol 45 (2) ◽  
pp. 253-260
Author(s):  
I.V. Zenkov ◽  
A.V. Lapko ◽  
V.A. Lapko ◽  
S.T. Im ◽  
V.P. Tuboltsev ◽  
...  

A nonparametric algorithm for automatic classification of large statistical data sets is proposed. The algorithm is based on a procedure for optimal discretization of the range of values of a random variable. A class is a compact group of observations of a random variable corresponding to a unimodal fragment of the probability density. The considered algorithm of automatic classification is based on the «compression» of the initial information based on the decomposition of a multidimensional space of attributes. As a result, a large statistical sample is transformed into a data array composed of the centers of multidimensional sampling intervals and the corresponding frequencies of random variables. To substantiate the optimal discretization procedure, we use the results of a study of the asymptotic properties of a kernel-type regression estimate of the probability density. An optimal number of sampling intervals for the range of values of one- and two-dimensional random variables is determined from the condition of the minimum root-mean square deviation of the regression probability density estimate. The results obtained are generalized to the discretization of the range of values of a multidimensional random variable. The optimal discretization formula contains a component that is characterized by a nonlinear functional of the probability density. An analytical dependence of the detected component on the antikurtosis coefficient of a one-dimensional random variable is established. For independent components of a multidimensional random variable, a methodology is developed for calculating estimates of the optimal number of sampling intervals for random variables and their lengths. On this basis, a nonparametric algorithm for the automatic classification is developed. It is based on a sequential procedure for checking the proximity of the centers of multidimensional sampling intervals and relationships between frequencies of the membership of the random variables from the original sample of these intervals. To further increase the computational efficiency of the proposed automatic classification algorithm, a multithreaded method of its software implementation is used. The practical significance of the developed algorithms is confirmed by the results of their application in processing remote sensing data.


2019 ◽  
pp. 16-20 ◽  
Author(s):  
A. V. Lapko ◽  
◽  
V. A. Lapko ◽  
◽  
◽  
...  

2021 ◽  
pp. 3-9
Author(s):  
Aleksandr V. Lapko ◽  
Vasiliy A. Lapko ◽  
Anna V. Bakhtina

The possibility of circumventing the problem of decomposition of the range of values of random variables when testing various hypotheses is considered. A brief review of the literature on this problem is given. A method for forming sets of independent components of a multidimensional random variable is proposed, based on hypotheses testing about the independence of paired combinations of components of a multidimensional random variable. The method uses a two-dimensional non-parametric algorithm for pattern recognition of the kernel type, corresponding to the criterion of maximum likelihood. In contrast to the traditional method based on the application of the Pearson criterion, the proposed approach avoids the problem of decomposing the range of values of random variables into multidimensional intervals. The results of computational experiments performed according to the method of forming sets of independent random variables are presented. Using the information obtained, an information graph is constructed, the vertices of which correspond to the components of a multidimensional random variable, and the edges determine their independence. Then the vertices of the complete subgraphs correspond to groups of independent components of a random variable. The obtained results form the basis for the synthesis of a multi-level nonparametric large volume data processing system, each level of which corresponds to a specific set of independent random variables.


2007 ◽  
Vol 382 (1) ◽  
pp. 71-83 ◽  
Author(s):  
Osman Hasan ◽  
Sofiène Tahar

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