Preface |
|
xi | |
1. POSITIVE POLYNOMIALS |
|
1 | |
|
|
1 | |
|
|
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 | |
|
|
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 | |
|
|
56 | |
|
2.12 Details and other facts |
|
|
57 | |
|
|
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 | |
|
|
78 | |
|
3.5.3 Optimality certificate |
|
|
81 | |
|
3.6 Gram matrices from partial bases |
|
|
84 | |
|
3.6.1 Sparse polynomials and Gram representation |
|
|
84 | |
|
|
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 | |
|
|
93 | |
|
3.9 The Gram pair parameterization |
|
|
94 | |
|
3.9.1 Basic Gram pair parameterization |
|
|
94 | |
|
|
96 | |
|
|
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 | |
|
|
125 | |
|
|
126 | |
|
|
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 | |
|
|
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 | |
|
|
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 | |
|
|
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 | |