What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical optimization
- Algorithm
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.
His most cited work include:
- A tutorial on decomposition methods for network utility maximization (1403 citations)
- Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization (1083 citations)
- Power Control By Geometric Programming (735 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Mathematical optimization (46.49%)
- MIMO (24.41%)
- Algorithm (21.74%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (21.74%)
- Mathematical optimization (46.49%)
- Portfolio (9.36%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning (384 citations)
- Transmit Waveform/Receive Filter Design for MIMO Radar With Multiple Waveform Constraints (56 citations)
- A Unified Framework for Low Autocorrelation Sequence Design via Majorization–Minimization (52 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Computer network
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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