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xxi | |
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Part I Mathematical Framework |
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On the Perron Root of Irreducible Matrices |
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3 | (58) |
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3 | (1) |
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Some Bounds on the Perron Root and their Applications |
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4 | (21) |
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Concavity of the Perron Root on Some Subsets of Irreducible Matrices |
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11 | (3) |
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Kullback-Leibler Divergence Characterization |
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14 | (1) |
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A Rate Function Representation for Large Deviations of Finite Dimensional Markov Chains |
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15 | (5) |
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Some Extended Perron Root Characterizations |
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20 | (2) |
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Collatz-Wielandt-Type Characterization of the Perron Root |
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22 | (3) |
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Convexity of the Perron Root |
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25 | (9) |
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26 | (2) |
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28 | (2) |
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Convexity of the Feasibility Set |
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30 | (2) |
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32 | (2) |
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Special Classes of Matrices |
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34 | (3) |
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35 | (1) |
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Symmetric Positive Semidefinite Matrices |
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36 | (1) |
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The Perron Root under the Linear Mapping |
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37 | (8) |
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39 | (3) |
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Disproof of the Conjecture |
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42 | (3) |
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The Perron Root under Exponential Mapping |
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45 | (6) |
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A Necessary and Sufficient Condition on Strict Convexity of the Feasibility Set |
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45 | (3) |
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Graph-theoretic Interpretation |
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48 | (3) |
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Generalizations to Arbitrary Nonnegative Matrices |
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51 | (8) |
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Log-Convexity of the Spectral Radius |
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52 | (1) |
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Characterization of the Spectral Radius |
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52 | (4) |
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Existence of Positive Eigenvectors |
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56 | (1) |
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Collatz-Wielandt-Type Characterization of the Spectral Radius |
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57 | (2) |
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59 | (2) |
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On the Positive Solution to a Linear System with Nonnegative Coefficients |
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61 | (20) |
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Basic Concepts and Definitions |
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61 | (2) |
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63 | (3) |
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66 | (10) |
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Log-Convexity of the Positive Solution |
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67 | (2) |
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Convexity of the Feasibility Set |
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69 | (1) |
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70 | (5) |
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Strict Convexity of the Feasibility Sets |
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75 | (1) |
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76 | (5) |
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Part II Principles of Resource Allocation in Wireless Networks |
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81 | (4) |
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85 | (34) |
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85 | (2) |
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87 | (3) |
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Wireless Communication Channel |
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90 | (29) |
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Signal-to-Interference Ratio |
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94 | (4) |
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Different Receiver Structures |
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98 | (6) |
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104 | (3) |
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107 | (4) |
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111 | (8) |
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Resource Allocation Problem in Communications Networks |
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119 | (142) |
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End-to-End Rate Control in Wired Networks |
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119 | (6) |
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120 | (4) |
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124 | (1) |
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Problem Formulation for Wireless Networks |
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125 | (25) |
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Joint Power Control and Link Scheduling |
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126 | (3) |
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129 | (3) |
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End-to-End Window-Based Rate Control |
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132 | (2) |
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MAC Layer Fair Rate Control |
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134 | (2) |
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Utility-Based Power Control |
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136 | (5) |
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Efficiency-Fairness Trade-Off |
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141 | (5) |
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146 | (4) |
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Interpretation in the QoS Domain |
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150 | (10) |
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Remarks on Joint Power Control and Link Scheduling |
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160 | (8) |
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Optimal Joint Power Control and Link Scheduling |
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160 | (3) |
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163 | (1) |
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163 | (4) |
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Wireless Links with Self-Interference |
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167 | (1) |
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168 | (23) |
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169 | (5) |
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Axiomatic Interference Functions |
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174 | (6) |
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QoS-Based Power Control Algorithms |
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180 | (11) |
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Max-Min SIR Balancing Power Control |
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191 | (19) |
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Some Preliminary Observations |
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192 | (3) |
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Characterization under Sum Power Constraints |
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195 | (4) |
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General Power Constraints |
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199 | (5) |
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Some Consequences and Applications |
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204 | (6) |
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Utility-based Power Control with QoS Support |
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210 | (12) |
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212 | (1) |
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213 | (9) |
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Utility-Based Joint Power and Receiver Control |
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222 | (6) |
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222 | (2) |
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224 | (2) |
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Decentralized Alternating Computation |
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226 | (1) |
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227 | (1) |
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Additional Results for a Noiseless Case |
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228 | (18) |
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The Efficiency-Fairness Trade-off |
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229 | (12) |
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Existence and Uniqueness of Log-SIR Fair Power Allocation |
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241 | (5) |
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246 | (15) |
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261 | (140) |
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261 | (1) |
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262 | (2) |
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Convex Statement of the Problem |
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264 | (2) |
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Strong Convexity Conditions |
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266 | (4) |
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Gradient Projection Algorithm |
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270 | (6) |
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270 | (3) |
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273 | (2) |
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275 | (1) |
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Projection on a Closed Convex Set |
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275 | (1) |
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Distributed Implementation |
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276 | (12) |
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Local and Global Parts of the Gradient Vector |
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276 | (2) |
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278 | (4) |
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Distributed Handshake Protocol |
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282 | (1) |
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283 | (2) |
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285 | (3) |
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Incorporation of QoS Requirements |
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288 | (14) |
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289 | (11) |
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300 | (2) |
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302 | (45) |
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Improving Efficiency by Primal-Dual Methods |
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304 | (7) |
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311 | (8) |
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319 | (3) |
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Decentralized Implementation |
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322 | (4) |
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Min-max Optimization Framework |
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326 | (16) |
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342 | (5) |
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Some Concepts and Results from Matrix Analysis |
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347 | (30) |
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347 | (2) |
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Matrices and Matrix Norms |
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349 | (2) |
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Square Matrices and Eigenvalues |
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351 | (2) |
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Matrix Spectrum, Spectral Radius and Neumann Series |
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353 | (2) |
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Orthogonal, Symmetric and Positive Semidefinite Matrices |
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355 | (2) |
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357 | (1) |
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Perron-Frobenius Theorem for Irreducible Matrices |
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358 | (4) |
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Perron-Frobenius Theorem for Primitive Matrices |
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362 | (1) |
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Some Extensions to Reducible Matrices |
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363 | (8) |
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The Existence of a Positive Solution p to (αI - X)p = b |
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371 | (6) |
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Some Concepts and Results from Convex Analysis |
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377 | (24) |
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377 | (6) |
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Convex Sets and Functions |
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383 | (1) |
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384 | (2) |
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Majorization and Schur-Convexity |
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386 | (1) |
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386 | (2) |
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Inverse Functions of Monotonic Log-Convex Functions |
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388 | (1) |
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Basics of Optimization Theory |
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389 | (1) |
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Characterization of Numerical Convergence |
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390 | (2) |
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Convergence of Gradient Projection Algorithms |
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392 | (3) |
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Basics of Lagrangian Optimization Theory |
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395 | (3) |
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Saddle Points, Saddle Functions, Min-Max Functions |
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398 | (3) |
References |
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401 | (10) |
Index |
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411 | |