His main research concerns Mathematical optimization, MIMO, Distributed algorithm, Game theory and Nash equilibrium. His research in the fields of Optimization problem overlaps with other disciplines such as Convex optimization. His MIMO study combines topics from a wide range of disciplines, such as Transmitter, Transmitter power output, Channel state information and Robustness.
His research in Distributed algorithm intersects with topics in Variational inequality, Theoretical computer science, Resource allocation and Power control. His studies deal with areas such as Electric energy, Iterative method, Energy and Uniqueness as well as Game theory. His Nash equilibrium study combines topics in areas such as Maximization and Asynchronous communication.
Daniel P. Palomar mainly investigates Mathematical optimization, MIMO, Algorithm, Convex optimization and Distributed algorithm. Daniel P. Palomar has researched Mathematical optimization in several fields, including Covariance matrix, Game theory and Robustness. His biological study spans a wide range of topics, including Transmitter, Electronic engineering, Channel state information and Control theory.
His Control theory research focuses on subjects like Precoding, which are linked to Communications system. His work in Algorithm addresses subjects such as Expectation–maximization algorithm, which are connected to disciplines such as Missing data. Daniel P. Palomar interconnects Convergence, Resource allocation, Asynchronous communication and Power control in the investigation of issues within Distributed algorithm.
Daniel P. Palomar mainly focuses on Algorithm, Mathematical optimization, Portfolio, Estimation theory and Theoretical computer science. His study in Algorithm is interdisciplinary in nature, drawing from both Time series, Outlier, Expectation–maximization algorithm, Waveform and Skewness. Particularly relevant to Optimization problem is his body of work in Mathematical optimization.
In the field of Portfolio, his study on Portfolio optimization overlaps with subjects such as Variance and Statistical arbitrage. His biological study deals with issues like Multivariate statistics, which deal with fields such as Estimator and Convergence. His Theoretical computer science research includes elements of Graphical model, Interpretability and Graph.
Algorithm, Mathematical optimization, Theoretical computer science, Linear programming and Algorithm design are his primary areas of study. His studies in Algorithm integrate themes in fields like Pixel, Waveform, Series and Metric. His research on Mathematical optimization focuses in particular on Optimization problem.
In his study, Transmitter and Wireless is inextricably linked to MIMO, which falls within the broad field of Optimization problem. His Theoretical computer science study incorporates themes from Matrix, Graphical model, Interpretability, Graph and Laplace operator. His Algorithm design research integrates issues from Radar, Constraint and Stationary point.
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A tutorial on decomposition methods for network utility maximization
D.P. Palomar;Mung Chiang.
IEEE Journal on Selected Areas in Communications (2006)
Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization
D.P. Palomar;J.M. Cioffi;M.A. Lagunas.
IEEE Transactions on Signal Processing (2003)
Power Control By Geometric Programming
M. Chiang;Chee Wei Tan;D.P. Palomar;D. O'Neill.
IEEE Transactions on Wireless Communications (2007)
Demand-Side Management via Distributed Energy Generation and Storage Optimization
I. Atzeni;L. G. Ordonez;G. Scutari;D. P. Palomar.
IEEE Transactions on Smart Grid (2013)
Practical algorithms for a family of waterfilling solutions
D.P. Palomar;J.R. Fonollosa.
IEEE Transactions on Signal Processing (2005)
Rank-Constrained Separable Semidefinite Programming With Applications to Optimal Beamforming
Yongwei Huang;D.P. Palomar.
IEEE Transactions on Signal Processing (2010)
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
Ying Sun;Prabhu Babu;Daniel P. Palomar.
IEEE Transactions on Signal Processing (2017)
Convex Optimization in Signal Processing and Communications
Daniel P. Palomar;Yonina C. Eldar.
Convex Optimization in Signal Processing and Communications (2009)
Alternative Distributed Algorithms for Network Utility Maximization: Framework and Applications
D.P. Palomar;Mung Chiang.
IEEE Transactions on Automatic Control (2007)
Convex Optimization, Game Theory, and Variational Inequality Theory
Gesualdo Scutari;Daniel Palomar;Francisco Facchinei;Jong-shi Pang.
IEEE Signal Processing Magazine (2010)
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