LEADER 05619nam 22007574a 450 001 9910143315903321 005 20210209180957.0 010 $a1-280-51061-7 010 $a9786610510610 010 $a1-84704-483-2 010 $a0-470-39464-1 010 $a0-470-61250-9 010 $a1-84704-583-9 035 $a(CKB)1000000000335550 035 $a(EBL)700743 035 $a(OCoLC)769341529 035 $a(SSID)ssj0000227838 035 $a(PQKBManifestationID)11225780 035 $a(PQKBTitleCode)TC0000227838 035 $a(PQKBWorkID)10269942 035 $a(PQKB)11186106 035 $a(MiAaPQ)EBC700743 035 $a(MiAaPQ)EBC261393 035 $a(MiAaPQ)EBC4036821 035 $a(Au-PeEL)EBL261393 035 $a(OCoLC)501313838 035 $a(EXLCZ)991000000000335550 100 $a20060110d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aProbabilistic combinatorial optimization on graphs$b[electronic resource] /$fCe?cile Murat and Vangelis Th. Paschos 210 $aLondon ;$aNewport Beach, CA $cISTE$d2006 215 $a1 online resource (269 p.) 225 1 $aISTE ;$vv.105 300 $aDescription based upon print version of record. 311 $a1-905209-33-9 320 $aIncludes bibliographical references (p. [255]-259) and index. 327 $aProbabilistic Combinatorial Optimization on Graphs; Contents; Preface; Chapter 1. A Short Insight into Probabilistic Combinatorial Optimization; 1.1. Motivations and applications; 1.2. A formalism for probabilistic combinatorial optimization; 1.3. The main methodological issues dealing with probabilistic combinatorial optimization; 1.3.1. Complexity issues; 1.3.1.1. Membership in NPO is not always obvious; 1.3.1.2. Complexity of deterministic vs. complexity of probabilistic optimization problems; 1.3.2. Solution issues; 1.3.2.1. Characterization of optimal a priori solutions 327 $a1.3.2.2. Polynomial subcases1.3.2.3. Exact solutions and polynomial approximation issues; 1.4. Miscellaneous and bibliographic notes; First Part. Probabilistic Graph-Problems; Chapter 2. The Probabilistic Maximum Independent Set; 2.1. The modification strategies and a preliminary result; 2.1.1. Strategy M1; 2.1.2. Strategies M2 and M3; 2.1.3. Strategy M4; 2.1.4. Strategy M5; 2.1.5. A general mathematical formulation for the five functionals; 2.2. PROBABILISTIC MAX INDEPENDENT SET1; 2.2.1. Computing optimal a priori solutions; 2.2.2. Approximating optimal solutions 327 $a2.2.3. Dealing with bipartite graphs2.3. PROBABILISTIC MAX INDEPENDENT SET2 and 3; 2.3.1. Expressions for E(G, S, M2) and E(G, S, M3); 2.3.2. An upper bound for the complexity of E(G, S, M2); 2.3.3. Bounds for E(G, S, M2); 2.3.4. Approximating optimal solutions; 2.3.4.1. Using argmax {?viESpi} as an a priori solution; 2.3.4.2. Using approximations of MAX INDEPENDENT SET; 2.3.5. Dealing with bipartite graphs; 2.4. PROBABILISTIC MAX INDEPENDENT SET4; 2.4.1. An expression for E(G, S, M4); 2.4.2. Using S* or argmax{?viESpi} as an a priori solution; 2.4.3. Dealing with bipartite graphs 327 $a2.5. PROBABILISTIC MAX INDEPENDENT SET52.5.1. In general graphs; 2.5.2. In bipartite graphs; 2.6. Summary of the results; 2.7. Methodological questions; 2.7.1. Maximizing a criterion associated with gain; 2.7.1.1. The minimum gain criterion; 2.7.1.2. The maximum gain criterion; 2.7.2. Minimizing a criterion associated with regret; 2.7.2.1. The maximum regret criterion; 2.7.3. Optimizing expectation; 2.8. Proofs of the results; 2.8.1. Proof of Proposition 2.1; 2.8.2. Proof of Theorem 2.6; 2.8.3. Proof of Proposition 2.3; 2.8.4. Proof of Theorem 2.13 327 $aChapter 3. The Probabilistic Minimum Vertex Cover3.1. The strategies M1, M2 and M3 and a general preliminary result; 3.1.1. Specification of M1, M2 and M3; 3.1.1.1. Strategy M1; 3.1.1.2. Strategy M2; 3.1.1.3. Strategy M3; 3.1.2. A first expression for the functionals; 3.2. PROBABILISTIC MIN VERTEX COVER1; 3.3. PROBABILISTIC MIN VERTEX COVER2; 3.4. PROBABILISTIC MIN VERTEX COVER3; 3.4.1. Building E(G, C, M3); 3.4.2. Bounds for E(G, C, M3); 3.5. Some methodological questions; 3.6. Proofs of the results; 3.6.1. Proof of Theorem 3.3; 3.6.2. On the the bounds obtained in Theorem 3.3 327 $aChapter 4. The Probabilistic Longest Path 330 $aThis title provides a comprehensive survey over the subject of probabilistic combinatorial optimization, discussing probabilistic versions of some of the most paradigmatic combinatorial problems on graphs, such as the maximum independent set, the minimum vertex covering, the longest path and the minimum coloring. Those who possess a sound knowledge of the subject mater will find the title of great interest, but those who have only some mathematical familiarity and knowledge about complexity and approximation theory will also find it an accessible and informative read. 410 0$aISTE 606 $aCombinatorial probabilities 606 $aCombinatorial optimization 606 $aRandom graphs 608 $aElectronic books. 615 0$aCombinatorial probabilities. 615 0$aCombinatorial optimization. 615 0$aRandom graphs. 676 $a511.6 676 $a519.2 700 $aMurat$b Cecile$0978945 701 $aPaschos$b Vangelis Th$0944252 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143315903321 996 $aProbabilistic combinatorial optimization on graphs$92231453 997 $aUNINA LEADER 06064nam 2200757Ia 450 001 9910437903203321 005 20200520144314.0 010 $a9786613944177 010 $a9781283631723 010 $a1283631725 010 $a9783642321320 010 $a3642321321 024 7 $a10.1007/978-3-642-32132-0 035 $a(CKB)2670000000250187 035 $a(EBL)1030512 035 $a(OCoLC)809201493 035 $a(SSID)ssj0000738372 035 $a(PQKBManifestationID)11420191 035 $a(PQKBTitleCode)TC0000738372 035 $a(PQKBWorkID)10791055 035 $a(PQKB)10031983 035 $a(DE-He213)978-3-642-32132-0 035 $a(MiAaPQ)EBC1030512 035 $z(PPN)258852828 035 $a(PPN)168321068 035 $a(EXLCZ)992670000000250187 100 $a20120821h20122013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMethods of signal processing for adaptive antenna arrays /$fLarysa Titarenko and Alexander Barkalov 205 $a1st ed. 2013. 210 $aBerlin ;$aHeidleberg $cSpringer-Verlag$d2012, c2013 215 $a1 online resource (233 p.) 225 0 $aSignals and communication technology ;$v6 300 $aDescription based upon print version of record. 311 08$a9783642428067 311 08$a3642428061 311 08$a9783642321313 311 08$a3642321313 320 $aIncludes bibliographical references and index. 327 $aTitle; Acknowledgements; Abbreviations; Introduction; General Characteristic of Methods for STSP; Analysis of Methods of Nonadaptive Spatial Signal Processing; Analysis of Peculiarities of Adaptive Spatial Signal Processing; Analysis of Non-structural Methods of Adaptive STSP; Analysis of Classical Structural Methods of Adaptive STSP; References; Background of Classical Theory of ASSP; Analysis of Typical Description of Signal-Noise Situation; Introduction into System of Criteria of Optimality; Analysis of Algorithms of Adaptive Space-Time Signal Processing; References 327 $aFeatures of ASSP under Different Levels of A-Priori UncertaintyAnalysis of Peculiarities of ASSP with Different Levels of A-Priori Uncertainty; Nature of a Priori Uncertainty about Properties of Signal and Noise; Methods of SSP under Generalized Parametric Uncertainty about the Noise Properties; Methods of SP under a Priory Parametric Uncertainty about Properties of Useful Signal; References; Algorithms of ASSP with Not Exactly Known Parameters; Main Approaches for Development of Algorithms of ASSP with Not Exactly Known Parameters 327 $aProbabilistic Approach for Synthesis of Robust Algorithms of ASSPDeterministic Approach: Robust Algorithms of ASSP for Modified Optimization Tasks; Restrictions for Value of Arbitrary Directivity Characteristic of Antenna; Additional Linear Restrictions; Restrictions of Standard Deviation for Directivity Characteristic of AA from the Given Value; Correlative Restrictions; Restrictions for the Shape of Amplitude-Phase Distribution of Currents in Channels of AA; Restriction for Value of Modulus of Output Signal of AA; Restrictions for Value of Norm ofWeight Coefficients 327 $aPeculiarities of Robustnization for Algorithms of ASSPApproximation of Control Vector by Section of Taylor Series; Projection Approach; Robustnization of ASSP Algorithms Using Nonlinear Transformations of Input Signals; Restrictions of Existed Methods of ASPS with Not Exactly Known Parameters; References; Background of ASSP with Not Exactly Known Parameters; Elements of Axiomatic and Some Analogies; Generalized Linear Systems of Rayleigh and Centrosymmetric Matrices; Algorithms of ASF Basing on Operators' Construction in Banach Space; Methods of Construction of Operators 327 $aMinimax Approach for Operator's Construction and Principle of ComparisonAdaptive Approach for Construction of Operators; Optimization Tasks with Squared Restrictions of the Unstrict Inequalities Type; Construction of Optimization Tasks with Mixed Restrictions; Construction of Optimization Tasks with Generalized Mixed Restrictions; Conclusion; References; Synthesis of ASF Algorithms for Not Exactly Known Parameters; Synthesis of Minimax Algorithms; Synthesis of Adaptive Algorithms; Synthesis of Algorithms for Adaptation of Structures of Operators to Current SIE 327 $aAnalysis of Quality for ASF Algorithms for Signals with Not Exactly Known Parameters 330 $aSo far there does not exist any theory of adaptive spatial signal processing (ASSP) for signals with uncertain parameters. This monograph is devoted to the development of this theory, which is very important in connection with wide spreading of telecommunications and radio links in the modern society. This theory can be applied for the development of effective radio communications. In the book some original approaches are proposed targeting the development of effective algorithms of ASSP with not exactly known parameters. They include both probabilistic and deterministic approaches for synthesis of robust algorithms of ASSP. The solution of problems also can be reduced to the construction of some operators for the Banach space which is presented in the book.  ?Methods of Signal Processing for Adaptive Antenna Arrays? targets professionals, students and PhD students in the area of telecommunications and should be useful for everybody connected with the new information technologies. 410 0$aSignals and Communication Technology,$x1860-4862 606 $aSignal processing 606 $aAdaptive antennas 615 0$aSignal processing. 615 0$aAdaptive antennas. 676 $a621.382/4 676 $a621.3822 700 $aTitarenko$b Larysa$01064371 701 $aBarkalov$b Alexander$0886717 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437903203321 996 $aMethods of signal processing for adaptive antenna arrays$94187547 997 $aUNINA