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Positive Trigonometric Polynomials and Signal Processing Applications 2007 ed. [Kietas viršelis]

  • Formatas: Hardback, 242 pages, aukštis x plotis: 235x155 mm, weight: 1190 g, XIV, 242 p., 1 Hardback
  • Serija: Signals and Communication Technology
  • Išleidimo metai: 08-Feb-2007
  • Leidėjas: Springer-Verlag New York Inc.
  • ISBN-10: 1402051247
  • ISBN-13: 9781402051241
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 242 pages, aukštis x plotis: 235x155 mm, weight: 1190 g, XIV, 242 p., 1 Hardback
  • Serija: Signals and Communication Technology
  • Išleidimo metai: 08-Feb-2007
  • Leidėjas: Springer-Verlag New York Inc.
  • ISBN-10: 1402051247
  • ISBN-13: 9781402051241
Kitos knygos pagal šią temą:
Positive and sum-of-squares polynomials have received a special interest in the latest decade, due to their connections with semidefinite programming. Thus, efficient optimization methods can be employed to solve diverse problems involving polynomials. This book gathers the main recent results on positive trigonometric polynomials within a unitary framework; the theoretical results are obtained partly from the general theory of real polynomials, partly from self-sustained developments. The optimization applications cover a field different from that of real polynomials, mainly in signal processing problems: design of 1-D and 2-D FIR or IIR filters, design of orthogonal filterbanks and wavelets, stability of multidimensional discrete-time systems.Positive Trigonometric Polynomials and Signal Processing Applications has two parts: theory and applications. The theory of sum-of-squares trigonometric polynomials is presented unitarily based on the concept of Gram matrix (extended to Gram pair or Gram set). The presentation starts by giving the main results for univariate polynomials, which are later extended and generalized for multivariate polynomials. The applications part is organized as a collection of related problems that use systematically the theoretical results. All the problems are brought to a semidefinite programming form, ready to be solved with algorithms freely available, like those from the library SeDuMi.

This book gathers the main recent results on positive trigonometric polynomials within a unitary framework. The book has two parts: theory and applications. The theory of sum-of-squares trigonometric polynomials is presented unitarily based on the concept of Gram matrix (extended to Gram pair or Gram set). The applications part is organized as a collection of related problems that use systematically the theoretical results.

Recenzijos

From the reviews:









"The book under review is a new contribution on the topic, with a focus on signal processing applications. this is the first self-contained manuscript on this emerging research area, and hence it is a welcome and timely contribution to the technical literature. use of illustrative numerical examples, accompanied by Matlab scripts, allows the inexperienced reader to grasp the essential ideas without having to understand all the mathematical subtelties. In particular, signal processing engineers should benefit a lot from reading the book ." (Didier Henrion, Zentralblatt MATH, Vol. 1126 (3), 2008)



"Trigonometric polynomials that are positive on the unit circle play an essential role in a number of digital filtering problems. The text would be quite suitable for use as the basis of lectures, for it has proofs written out, a survey of the literature and many exercises." (A. Bultheel, Mathematical Reviews, Issue 2007 m)

Preface xi
1. POSITIVE POLYNOMIALS 1
1.1 Types of polynomials
1
1.2 Positive polynomials
3
1.3 Toeplitz positivity conditions
8
1.4 Positivity on an interval
10
1.5 Details and other facts
14
1.5.1 Chebyshev polynomials
14
1.5.2 Positive polynomials in R[ t] as sum-of-squares
14
1.5.3 Proof of Theorem 1.11
15
1.5.4 Proof of Theorem 1.13
16
1.5.5 Proof of Theorem 1.15
17
1.5.6 Proof of Theorem 1.17
18
1.5.7 Proof of Theorem 1.18
19
1.6 Bibliographical and historical notes
19
2. GRAM MATRIX REPRESENTATION 21
2.1 Parameterization of trigonometric polynomials
21
2.2 Optimization using the trace parameterization
26
2.3 Toeplitz quadratic optimization
30
2.4 Duality
32
2.5 Kalman-Yakubovich-Popov lemma
33
2.6 Spectral factorization from a Gram matrix
35
2.6.1 SDP computation of a rank-1 Gram matrix
35
2.6.2 Spectral factorization using a Riccati equation
37
2.7 Parameterization of real polynomials
39
2.8 Choosing the right basis
42
2.8.1 Basis of trigonometric polynomials
42
2.8.2 Transformation to real polynomials
46
2.8.3 Gram pair matrix parameterization
47
2.9 Interpolation representations
51
2.10 Mixed representations
53
2.10.1 Complex polynomials and the DFT
54
2.10.2 Cosine polynomials and the DCT
55
2.11 Fast algorithms
56
2.12 Details and other facts
57
2.12.1 A SeDuMi program
57
2.12.2 Proof of Theorem 2.16
58
2.12.3 Proof of Theorem 2.19
60
2.12.4 Proof of Theorem 2.21
60
2.13 Bibliographical and historical notes
61
3. MULTIVARIATE POLYNOMIALS 65
3.1 Multivariate polynomials
66
3.2 Sum-of-squares multivariate polynomials
68
3.3 Sum-of-squares of real polynomials
71
3.4 Gram matrix parameterization of multivariate trigonometric polynomials
73
3.5 Sum-of-squares relaxations
77
3.5.1 Relaxation principle
77
3.5.2 A case study
78
3.5.3 Optimality certificate
81
3.6 Gram matrices from partial bases
84
3.6.1 Sparse polynomials and Gram representation
84
3.6.2 Relaxations
87
3.7 Gram matrices of real multivariate polynomials
88
3.7.1 Gram parameterization
88
3.7.2 Sum-of-squares relaxations
89
3.7.3 Sparseness treatment
90
3.8 Pairs of relaxations
93
3.9 The Gram pair parameterization
94
3.9.1 Basic Gram pair parameterization
94
3.9.2 Parity discussion
96
3.9.3 LMI form
97
3.10 Polynomials with matrix coefficients
101
3.11 Details and other facts
105
3.11.1 Transformation between trigonometric and real nonnegative polynomials
105
3.11.2 A program using the Gram pair parameterization
107
3.12 Bibliographical and historical notes
109
4. POLYNOMIALS POSITIVE ON DOMAINS 115
4.1 Real polynomials positive on compact domains
115
4.2 Trigonometric polynomials positive on frequency domains
119
4.2.1 Gram set parameterization
121
4.2.2 Gram-pair set parameterization
124
4.3 Bounded Real Lemma
125
4.3.1 Gram set BRL
126
4.3.2 Gram-pair set BRL
129
4.4 Positivstellensatz for trigonometric polynomials
130
4.5 Proof of Theorem 4.11
133
4.6 Bibliographical and historical notes
135
5. DESIGN OF FIR FILTERS 137
5.1 Design of FIR filters
137
5.1.1 Optimization of linear-phase FIR filters
139
5.1.2 Magnitude optimization
141
5.1.3 Approximate linear phase FIR filters
142
5.2 Design of 2-D FIR filters
145
5.2.1 2-D frequency domains
146
5.2.2 Linear phase designs
147
5.2.3 Approximate linear phase designs
152
5.3 FIR deconvolution
154
5.3.1 Basic optimization problem
156
5.3.2 Deconvolution of periodic FIR filters
157
5.3.3 Robust Hinfinity deconvolution
159
5.3.4 2-D Hinfinity deconvolution
160
5.4 Bibliographical and historical notes
162
6. ORTHOGONAL FILTERBANKS 167
6.1 Two-channel filterbanks
167
6.1.1 Orthogonal FB design
169
6.1.2 Towards signal-adapted wavelets
172
6.1.3 Design of symmetric complex-valued FBs
176
6.2 GDFT modulated filterbanks
180
6.2.1 GDFT modulation: definitions and properties
181
6.2.2 Design of near-orthogonal GDFT modulated FBs
185
6.3 Bibliographical and historical notes
187
7. STABILITY 191
7.1 Multidimensional stability tests
191
7.1.1 Stability test via positivity
192
7.1.2 Stability of Fornasini-Marchesini model
194
7.1.3 Positivstellensatz for testing stability
196
7.2 Robust stability
198
7.2.1 Real polynomials test
199
7.2.2 Trigonometric polynomials test
201
7.3 Convex stability domains
203
7.3.1 Positive realness stability domain
203
7.3.2 Comparisons and examples
207
7.3.3 Proof of Theorem 7.16
207
7.4 Bibliographical and historical notes
209
8. DESIGN OF IIR FILTERS 211
8.1 Magnitude design of IIR filters
211
8.2 Approximate linear-phase designs
213
8.2.1 Optimization with fixed denominator
215
8.2.2 IIR filter design using convex stability domains
217
8.3 2-D IIR filter design
220
8.4 Bibliographical and historical notes
222
Appendix A: semidefinite programming 225
Appendix B: spectral factorization 227
B.1 Root finding
227
B.2 Newton-Raphson algorithm
228
B.3 Factorization of banded Toeplitz matrices
229
B.4 Hilbert transform method
229
B.5 Polynomials with matrix coefficients
230
References 231
Index 239