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Опубліковано: 14 березня 2023

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Статті в журналах з теми "Determinantal"

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Dey, Papri. "Definite determinantal representations of multivariate polynomials." Journal of Algebra and Its Applications 19, no. 07 (July 23, 2019): 2050129. http://dx.doi.org/10.1142/s0219498820501297.

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In this paper, we consider the problem of representing a multivariate polynomial as the determinant of a definite (monic) symmetric/Hermitian linear matrix polynomial (LMP). Such a polynomial is known as determinantal polynomial. Determinantal polynomials can characterize the feasible sets of semidefinite programming (SDP) problems that motivates us to deal with this problem. We introduce the notion of generalized mixed discriminant (GMD) of matrices which translates the determinantal representation problem into computing a point of a real variety of a specified ideal. We develop an algorithm to determine such a determinantal representation of a bivariate polynomial of degree [Formula: see text]. Then we propose a heuristic method to obtain a monic symmetric determinantal representation (MSDR) of a multivariate polynomial of degree [Formula: see text].
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2

Kyrchei, Ivan I. "Determinantal Representations of the Core Inverse and Its Generalizations with Applications." Journal of Mathematics 2019 (October 1, 2019): 1–13. http://dx.doi.org/10.1155/2019/1631979.

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In this paper, we give the direct method to find of the core inverse and its generalizations that is based on their determinantal representations. New determinantal representations of the right and left core inverses, the right and left core-EP inverses, and the DMP, MPD, and CMP inverses are derived by using determinantal representations of the Moore-Penrose and Drazin inverses previously obtained by the author. Since the Bott-Duffin inverse has close relation with the core inverse, we give its determinantal representation and its application in finding solutions of the constrained linear equations that is an analog of Cramer’s rule. A numerical example to illustrate the main result is given.
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3

Marcus, Marvin. "Determinantal Loci." College Mathematics Journal 23, no. 1 (January 1992): 44. http://dx.doi.org/10.2307/2686198.

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4

Fulton, William. "determinantal formulas." Duke Mathematical Journal 65, no. 3 (March 1992): 381–420. http://dx.doi.org/10.1215/s0012-7094-92-06516-1.

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5

Beauville, Arnaud. "Determinantal hypersurfaces." Michigan Mathematical Journal 48, no. 1 (2000): 39–64. http://dx.doi.org/10.1307/mmj/1030132707.

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6

Marcus, Marvin. "Determinantal Loci." College Mathematics Journal 23, no. 1 (January 1992): 44–47. http://dx.doi.org/10.1080/07468342.1992.11973433.

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7

Kimura, Kenichiro, Shun-ichi Kimura, and Nobuyoshi Takahashi. "Motivic zeta functions in additive monoidal categories." Journal of K-theory 9, no. 3 (December 8, 2011): 459–73. http://dx.doi.org/10.1017/is011011006jkt174.

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AbstractLet C be a pseudo-abelian symmetric monoidal category, and X a Schur-finite object of C. We study the problem of rationality of the motivic zeta function ζx(t) of X. Since the coefficient ring is not a field, there are several variants of rationality — uniform, global, determinantal and pointwise rationality. We show that ζx(t) is determinantally rational, and we give an example of C and X for which the motivic zeta function is not uniformly rational.
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8

Stanimirović, Predrag S., and Milan Lj Zlatanović. "Determinantal Representation of Outer Inverses in Riemannian Space." Algebra Colloquium 19, spec01 (October 31, 2012): 877–92. http://dx.doi.org/10.1142/s1005386712000740.

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Starting from a known determinantal representation of outer inverses, we derive their determinantal representation in terms of the inner product in the Euclidean space. We define the double inner product of two miscellaneous tensors of rank 2 in a Riemannian space. The corresponding determinantal representation as well as the general representation of outer inverses in the Riemannian space are derived. A non-zero {2}-inverse X of a given tensor A obeying ρ(X) = s with 1 ≤ s ≤ r = ρ(A) is expressed in terms of the double inner product involving compound tensors with minors of order s, extracted from A and appropriate tensors.
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9

Conca, Aldo. "Symmetric ladders." Nagoya Mathematical Journal 136 (December 1994): 35–56. http://dx.doi.org/10.1017/s0027763000024958.

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In this paper we define and study ladder determinantal rings of a symmetric matrix of indeterminates. We show that they are Cohen-Macaulay domains. We give a combinatorial characterization of their h-vectors and we compute the a-invariant of the classical determinantal rings of a symmetric matrix of indeterminates.
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10

Hardy, Adrien, and Mylène Maïda. "Determinantal Point Processes." EMS Newsletter 2019-6, no. 112 (June 6, 2019): 8–15. http://dx.doi.org/10.4171/news/112/3.

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Дисертації з теми "Determinantal"

1

Konvalinka, Matjaž. "Combinatorics of determinantal identities." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43790.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2008.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 125-129).
In this thesis, we apply combinatorial means for proving and generalizing classical determinantal identities. In Chapter 1, we present some historical background and discuss the algebraic framework we employ throughout the thesis. In Chapter 2, we construct a fundamental bijection between certain monomials that proves crucial for most of the results that follow. Chapter 3 studies the first, and possibly the best-known, determinantal identity, the matrix inverse formula, both in the commutative case and in some non-commutative settings (Cartier-Foata variables, right-quantum variables, and their weighted generalizations). We give linear-algebraic and (new) bijective proofs; the latter also give an extension of the Jacobi ratio theorem. Chapter 4 is dedicated to the celebrated MacMahon master theorem. We present numerous generalizations and applications. In Chapter 5, we study another important result, Sylvester's determinantal identity. We not only generalize it to non-commutative cases, we also find a surprising extension that also generalizes the master theorem. Chapter 6 has a slightly different, representation theory flavor; it involves representations of the symmetric group, and also Hecke algebras and their characters. We extend a result on immanants due to Goulden and Jackson to a quantum setting, and reprove certain combinatorial interpretations of the characters of Hecke algebras due to Ram and Remmel.
by Matjaž Konvalinka.
Ph.D.
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2

Petroulakis, G. "The approximate Determinantal Assignment Problem." Thesis, City University London, 2015. http://openaccess.city.ac.uk/11894/.

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The Determinantal Assignment Problem (DAP) is one of the central problems of Algebraic Control Theory and refers to solving a system of non-linear algebraic equations to place the critical frequencies of the system to specied locations. This problem is decomposed into a linear and a multi-linear subproblem and the solvability of the problem is reduced to an intersection of a linear variety with the Grassmann variety. The linear subproblem can be solved with standard methods of linear algebra, whereas the intersection problem is a problem within the area of algebraic geometry. One of the methods to deal with this problem is to solve the linear problem and then and which element of this linear space is closer - in terms of a metric - to the Grassmann variety. If the distance is zero then a solution for the intersection problem is found, otherwise we get an approximate solution for the problem, which is referred to as the approximate DAP. In this thesis we examine the second case by introducing a number of new tools for the calculation of the minimum distance of a given parametrized multi-vector that describes the linear variety implied by the linear subproblem, from the Grassmann variety as well as the decomposable vector that realizes this least distance, using constrained optimization techniques and other alternative methods, such as the SVD properties of the so called Grassmann matrix, polar decompositions and mother tools. Furthermore, we give a number of new conditions for the appropriate nature of the approximate polynomials which are implied by the approximate solutions based on stability radius results. The approximate DAP problem is completely solved in the 2-dimensional case by examining uniqueness and non-uniqueness (degeneracy) issues of the decompositions, expansions to constrained minimization over more general varieties than the original ones (Generalized Grassmann varieties), derivation of new inequalities that provide closed-form non-algorithmic results and new stability radii criteria that test if the polynomial implied by the approximate solution lies within the stability domain of the initial polynomial. All results are compared with the ones that already exist in the respective literature, as well as with the results obtained by Algebraic Geometry Toolboxes, e.g., Macaulay 2. For numerical implementations, we examine under which conditions certain manifold constrained algorithms, such as Newton's method for optimization on manifolds, could be adopted to DAP and we present a new algorithm which is ideal for DAP approximations. For higher dimensions, the approximate solution is obtained via a new algorithm that decomposes the parametric tensor which is derived by the system of linear equations we mentioned before.
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3

Pereira, Miriam da Silva. "Variedades determinantais e singularidades de matrizes." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/55/55135/tde-22062010-133339/.

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O teorema de Hilbert-Burch fornece uma boa descrição de variedades determinantais de codi- mensão dois e de suas deformações em termos da matriz de representação. Neste trabalho, usamos esta correspondência para estudar propriedades de tais variedades usando métodos da teoria de singularidades. Na primeira parte da tese, estabelecemos a teoria de singularidades de matrizes n X p, generalizando os resultados obtidos por J. W. Bruce and F. Tari em [5], para ma- trizes quadradas, e por A. Frühbis-Krüger em [16], para matrizes n X (n+1). Na segunda parte, nos concentramos em variedades determinantais de codimensão 2, com singularidade isolada na origem. Para estas variedades, podemos mostrar a existência e a unicidade de suavizações, o que possibilita definir seu número de Milnor como o número de Betti na dimensão média de sua fibra genérica. Para superfícies em \'C POT. 4\', obtemos uma fórmula Lê-Greuel expressando o número de Milnor da superfície em termos da segunda multiplicidade polar e do número de Milnor de uma seção genérica
The theorem of Hilbert- Burch provides a good description of codimension two determinantal varieties and their deformations in terms of their presentation matrices. In this work we use this correspondence to study properties of determinantal varieties, based on methods of singularity theory of their presentation matrices. In the first part of the thesis we establish the theory of singularities for n X p matrices extending previous results of J. W. Bruce and F. Tari in [5], for classes of square matrices, and A. Frühbis-Krüger for n X (n+1) matrices in [16]. In the second part we concentrate on codimension two determinantal varieties with isolated singularities. These singularities admit a unique smoothing, thus we can define their Milnor number as the middle Betti number of their generic fiber. For surfaces in \'C POT. 4\' , we obtain a Lê-Greuel formula expressing the Milnor number of the surface in terms of the second polar multiplicity and the Milnor number of the generic section
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4

Piontkowski, Jens. "Compactified Jacobians and symmetric determinantal hypersurfaces." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973256419.

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5

Hägg, Jonas. "Gaussian fluctuations in some determinantal processes." Doctoral thesis, KTH, Matematik (Inst.), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4343.

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This thesis consists of two parts, Papers A and B, in which some stochastic processes, originating from random matrix theory (RMT), are studied. In the first paper we study the fluctuations of the kth largest eigenvalue, xk, of the Gaussian unitary ensemble (GUE). That is, let N be the dimension of the matrix and k depend on N in such a way that k and N-k both tend to infinity as N - ∞. The main result is that xk, when appropriately rescaled, converges in distribution to a Gaussian random variable as N → ∞. Furthermore, if k1 < ...< km are such that k1, ki+1 - ki and N - km, i =1, ... ,m - 1, tend to infinity as N → ∞ it is shown that (xk1 , ... , xkm) is multivariate Gaussian in the rescaled N → ∞ limit. In the second paper we study the Airy process, A(t), and prove that it fluctuates like a Brownian motion on a local scale. We also prove that the Discrete polynuclear growth process (PNG) fluctuates like a Brownian motion in a scaling limit smaller than the one where one gets the Airy process.
QC 20100716
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Hägg, Jonas. "Gaussian fluctuations in some determinantal processes /." Stockholm : Matematik, Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4343.

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7

Kennerberg, Philip. "Simulation of interpolating determinantal point processes." Thesis, KTH, Matematik (Avd.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167972.

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In this master thesis I aim to present some of the basic theory of determinantal processes. Some preliminary theory of random measures and point-process theory is reviewed in the first chapter. In the second chapter I introduce the notion of a determinantal process, through what is called trace-class kernels. I mention a few of the most fundamental theorems from the field and go through some useful theorems for determinantal processes concerning interpolation between different processes. An algorithm for simulating determinantal processes was suggested earlier in [9]. I study this algorithm and derive more explicit formulas for implementation. The algorithm is however based on some assumptions that the underlying process is of a specific form. There is however a way to get around this assumption in order to study a wider class of processes, using another result from [9]. I will study some processes that interpolate between well-known processes, and use my implemented simulation tool to study how this interpolation manifests itself.
I denna magisteruppsats presenterar jag lite av den teori som ligger till grund för determinantprocesser. I det första kapitlet går jag igenom en del av den grundläggande teorin kring slumpmått och punktprocesser. I det andra kapitlet introduceras begreppet determinantprocess via så kallade trace-class kärnor. Jag tar upp några av de mest fundamentala satserna i ämnet och några användbara satser för interpolation mellan olika determinantprocesser. En algoritm för simulering av determinantprocesser föreslogs tidigare i [9]. Jag studerar den algoritm och härleder mer explicita formler för implementering. Algoritmen I fråga bygger dock på att den underliggande processen är av en specifik form. Det finns emellertid ett sätt att komma runt detta antagande för att studera en större klass av processer, genom att använda ytterligare ett resultat från [9]. Jag studerar vidare två interpolerande processer som båda interpolerar mellan två andra välkända processer, och använder en implementering av de former jag härlett för simulering, för att undersöka hur interpolation ter sig. På senare tid har determinantprocesser funnit tillämpningar inom det datavetenskapliga ämnet maskininlärning. Inom maskininlärning simulerar man determinantprocesser för att utnyttja deras probabilistiska egenskaper. Det skulle därför kunna vara tänkbart att simuleringsmetoderna (eventuellt även de processer som studeras) som utvecklas här skulle kunna tillämpas inom detta ämne.
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8

Zach, Matthias [Verfasser]. "Topological invariants of isolated determinantal singularities / Matthias Zach." Hannover : Technische Informationsbibliothek (TIB), 2017. http://d-nb.info/1150664274/34.

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9

Mariet, Zelda Elaine. "Learning and enforcing diversity with Determinantal Point Processes." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103671.

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Анотація:
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 63-66).
As machine-learning techniques continue to require more data and become increasingly memory-heavy, being able to choose a subset of relevant, high-quality and diverse elements among large amounts of redundant or noisy data and parameters has become an important concern. Here, we approach this problem using Determinantal Point Processes (DPPs), probabilistic models that provide an intuitive and powerful way of balancing quality and diversity in sets of items. We introduce a novel, fixed-point algorithm for estimating the maximum likelihood parameters of a DPP, provide proof of convergence and discuss generalizations of this technique. We then apply DPPs to the difficult problem of detecting and eliminating redundancy in fully-connected layers of neural networks. By placing a DPP over a layer, we are able to sample a subset of neurons that perform non-overlapping computations and merge all other neurons of the layer into the previous diverse subset. This allows us to significantly reduce the size of the neural network while simultaneously maintaining a good performance.
by Zelda Elaine Mariet.
S.M.
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10

Naldi, Simone. "Exact algorithms for determinantal varieties and semidefinite programming." Thesis, Toulouse, INSA, 2015. http://www.theses.fr/2015ISAT0021/document.

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Dans cette thèse, nous nous intéressons à l'étude des structures déterminantielles apparaissent dans l'optimisation semi-définie (SDP), le prolongement naturel de la programmation linéaire au cône des matrices symétrique semi-définie positives. Si l'approximation d'une solution d'un programme semi-défini peut être calculé efficacement à l'aide des algorithmes de points intérieurs, ni des algorithmes exacts efficaces pour la SDP sont disponibles, ni une compréhension complète de sa complexité théorique a été atteinte. Afin de contribuer à cette question centrale en optimisation convexe, nous concevons un algorithme exact pour décider la faisabilité d'une inégalité matricielle linéaire (LMI) $A(x)\succeq 0$. Quand le spectraèdre associé (le lieu $\spec$ des $x \in \RR^n$ ou $A(x)\succeq 0$) n'est pas vide, la sortie de cet algorithme est une représentation algébrique d'un ensemble fini qui contient au moins un point $x \in \spec$: dans ce cas, le point $x$ minimise le rang de $A(x)$ sur $\spec$. La complexité est essentiellement quadratique en le degré de la représentation en sortie, qui coïncide, expérimentalement, avec le degré algébrique de l'optimisation semi-définie. C'est un garantie d'optimalité de cette approche dans le contexte des algorithmes exacts pour les LMI et la SDP. Remarquablement, l'algorithme ne suppose pas la présence d'un point intérieur dans $\spec$, et il profite de l'existence de solutions de rang faible de l'LMI $A(x)\succeq 0$. Afin d'atteindre cet objectif principal, nous développons une approche systématique pour les variétés déterminantielles associées aux matrices linéaires. Nous prouvons que décider la faisabilité d'une LMI $A(x)\succeq 0$ se réduit à calculer des points témoins dans les variétés déterminantielles définies sur $A(x)$. Nous résolvons ce problème en concevant un algorithme exact pour calculer au moins un point dans chaque composante connexe réelle du lieu des chutes de rang de $A(x)$. Cet algorithme prend aussi avantage des structures supplémentaires, et sa complexité améliore l'état de l'art en géométrie algébrique réelle. Enfin, les algorithmes développés dans cette thèse sont implantés dans une nouvelle bibliothèque Maple appelé Spectra, et les résultats des expériences mettant en évidence la meilleure complexité sont fournis
In this thesis we focus on the study of determinantal structures arising in semidefinite programming (SDP), the natural extension of linear programming to the cone of symetric positive semidefinite matrices. While the approximation of a solution of a semidefinite program can be computed efficiently by interior-point algorithms, neither efficient exact algorithms for SDP are available, nor a complete understanding of its theoretical complexity has been achieved. In order to contribute to this central question in convex optimization, we design an exact algorithm for deciding the feasibility of a linear matrix inequality (LMI) $A(x) \succeq 0$. When the spectrahedron $\spec = \{x \in \RR^n \mymid A(x) \succeq 0\}$ is not empty, the output of this algorithm is an algebraic representation of a finite set meeting $\spec$ in at least one point $x^*$: in this case, the point $x^*$ minimizes the rank of the pencil on the spectrahedron. The complexity is essentially quadratic in the degree of the output representation, which meets, experimentally, the algebraic degree of semidefinite programs associated to $A(x)$. This is a guarantee of optimality of this approach in the context of exact algorithms for LMI and SDP. Remarkably, the algorithm does not assume the presence of an interior point in the spectrahedron, and it takes advantage of the existence of low rank solutions of the LMI. In order to reach this main goal, we develop a systematic approach to determinantal varieties associated to linear matrices. Indeed, we prove that deciding the feasibility of a LMI can be performed by computing a sample set of real solutions of determinantal polynomial systems. We solve this problem by designing an exact algorithm for computing at least one point in each real connected component of the locus of rank defects of a pencil $A(x)$. This algorithm admits as input generic linear matrices but takes also advantage of additional structures, and its complexity improves the state of the art in computational real algebraic geometry. Finally, the algorithms developed in this thesis are implemented in a new Maple library called {Spectra}, and results of experiments highlighting the complexity gain are provided
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Книги з теми "Determinantal"

1

Cornel, Baetica, ed. Combinatorics of determinantal ideals. New York: Nova Publishers, 2006.

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2

Bruns, Winfried, and Udo Vetter. Determinantal Rings. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0080378.

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Bruns, Winfried. Determinantal rings. Berlin: Springer-Verlag, 1988.

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4

Determinantal ideals. Basel: Birkhäuser, 2008.

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5

Iarrobino, Anthony, and Vassil Kanev. Power Sums, Gorenstein Algebras, and Determinantal Loci. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0093426.

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6

Hough, J. Ben. Zeros of Gaussian analytic functions and determinantal point processes. Providence, R.I: American Mathematical Society, 2009.

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7

1979-, Hough J. Ben, ed. Zeros of Gaussian analytic functions and determinantal point processes. Providence, R.I: American Mathematical Society, 2009.

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8

1979-, Hough J. Ben, ed. Zeros of Gaussian analytic functions and determinantal point processes. Providence, R.I: American Mathematical Society, 2009.

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9

Kebza, Vladimír. Psychosociální determinanty zdraví: Psychosocial determinants of health. Praha: Academia, 2005.

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10

Jerzy, Mierzejewski Donat, and Jan Polcyn. Rozwój regionalny i jego determinanty: Regional development and its determinants. Piła: Państwowa Wyższa Szkoła Zawodowa im. Stanisława Staszica, 2014.

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Частини книг з теми "Determinantal"

1

Harris, Joe. "Determinantal Varieties." In Algebraic Geometry, 98–113. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4757-2189-8_9.

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2

Arbarello, E., M. Cornalba, P. A. Griffiths, and J. Harris. "Determinantal Varieties." In Grundlehren der mathematischen Wissenschaften, 61–106. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4757-5323-3_2.

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3

Lakshmibai, V., and Justin Brown. "Determinantal Varieties." In The Grassmannian Variety, 143–53. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-3082-1_10.

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4

Weiss, Thomas, Patrik Ferrari, and Herbert Spohn. "Determinantal Point Processes." In Reflected Brownian Motions in the KPZ Universality Class, 25–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49499-9_3.

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Decreusefond, Laurent, Ian Flint, Nicolas Privault, and Giovanni Luca Torrisi. "Determinantal Point Processes." In Stochastic Analysis for Poisson Point Processes, 311–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-05233-5_10.

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6

Hough, J., Manjunath Krishnapur, Yuval Peres, and Bálint Virág. "Determinantal point processes." In University Lecture Series, 47–81. Providence, Rhode Island: American Mathematical Society, 2009. http://dx.doi.org/10.1090/ulect/051/04.

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Hough, J., Manjunath Krishnapur, Yuval Peres, and Bálint Virág. "A determinantal zoo." In University Lecture Series, 99–117. Providence, Rhode Island: American Mathematical Society, 2009. http://dx.doi.org/10.1090/ulect/051/06.

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8

Constantinescu, Tiberiu. "Determinantal Formulae and Optimization." In Schur Parameters, Factorization and Dilation Problems, 203–22. Basel: Birkhäuser Basel, 1996. http://dx.doi.org/10.1007/978-3-0348-9108-0_8.

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9

Qiao, Youming, Xiaoming Sun, and Nengkun Yu. "Determinantal Complexities and Field Extensions." In Algorithms and Computation, 119–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45030-3_12.

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Mitrinović, D. S., J. E. Pečarić, and A. M. Fink. "Some Determinantal and Matrix Inequalities." In Classical and New Inequalities in Analysis, 211–38. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-017-1043-5_8.

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Тези доповідей конференцій з теми "Determinantal"

1

Meister, Clara, Martina Forster, and Ryan Cotterell. "Determinantal Beam Search." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.512.

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2

Tremblay, Nicolas, Pierre-Olivier Amblard, and Simon Barthelme. "Graph sampling with determinantal processes." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081494.

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3

Warlop, Romain, Jérémie Mary, and Mike Gartrell. "Tensorized Determinantal Point Processes for Recommendation." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330952.

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4

Gartrell, Mike, Ulrich Paquet, and Noam Koenigstein. "Bayesian Low-Rank Determinantal Point Processes." In RecSys '16: Tenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2959100.2959178.

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5

Liu, Yuli, Christian Walder, and Lexing Xie. "Determinantal Point Process Likelihoods for Sequential Recommendation." In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3477495.3531965.

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Sharma, Ganesh, and Subhrakanti Dey. "On Analog Distributed Approximate Newton with Determinantal Averaging." In 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2022. http://dx.doi.org/10.1109/pimrc54779.2022.9977466.

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7

Blaszczyszyn, B., and H. P. Keeler. "Determinantal thinning of point processes with network learning applications." In 2019 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2019. http://dx.doi.org/10.1109/wcnc.2019.8885526.

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8

Li, Yingzhe, Francois Baccelli, Harpreet S. Dhillon, and Jeffrey G. Andrews. "Fitting determinantal point processes to macro base station deployments." In GLOBECOM 2014 - 2014 IEEE Global Communications Conference. IEEE, 2014. http://dx.doi.org/10.1109/glocom.2014.7037373.

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9

Wilhelm, Mark, Ajith Ramanathan, Alexander Bonomo, Sagar Jain, Ed H. Chi, and Jennifer Gillenwater. "Practical Diversified Recommendations on YouTube with Determinantal Point Processes." In CIKM '18: The 27th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3269206.3272018.

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Li, Lei, Zuying Huang, and Yazhao Zhang. "Quality-Diversity Automatic Summarization based on Determinantal Point Processes." In 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2019. http://dx.doi.org/10.1109/itaic.2019.8785485.

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Звіти організацій з теми "Determinantal"

1

Barro, Robert, and Rachel McCleary. International Determinants of Religiosity. Cambridge, MA: National Bureau of Economic Research, December 2003. http://dx.doi.org/10.3386/w10147.

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2

Lopez-de-Silane, Florencio. Determinants of Privatization Prices. Cambridge, MA: National Bureau of Economic Research, March 1996. http://dx.doi.org/10.3386/w5494.

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3

Alesina, Alberto, and Eliana La Ferrara. The Determinants of Trust. Cambridge, MA: National Bureau of Economic Research, March 2000. http://dx.doi.org/10.3386/w7621.

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4

Cutler, David, Angus Deaton, and Adriana Lleras-Muney. The Determinants of Mortality. Cambridge, MA: National Bureau of Economic Research, January 2006. http://dx.doi.org/10.3386/w11963.

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5

Chu, Lisa W. Dietary Determinants of Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada439274.

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Mitchell, Cynthia L. Weight Maintenance: Determinants of Success. Fort Belvoir, VA: Defense Technical Information Center, December 2005. http://dx.doi.org/10.21236/ada441738.

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Dingel, Jonathan. The Determinants of Quality Specialization. Cambridge, MA: National Bureau of Economic Research, October 2016. http://dx.doi.org/10.3386/w22757.

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Simpson, Jennifer K., Francesmary Modugno, Joel L. Weissfeld, Lewis Kuller, Victor Vogel, and Joseph P. Costantino. Hormonal Determinants of Mammographic Density. Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada432434.

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9

Horne, David K., and Mary Weltin. Determinants of Army Career Intentions. Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada178672.

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10

Porta, Rafael La, Florencio Lopez-de-Silane, Andrei Shleifer, and Robert Vishny. Legal Determinants of External Finance. Cambridge, MA: National Bureau of Economic Research, January 1997. http://dx.doi.org/10.3386/w5879.

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