Quadratic programming (QP) is the process of solving certain mathematical optimization problems involving quadratic functions. Specifically, one seeks to optimize a multivariate quadratic function subject to linear constraints on the variables. Quadratic programming is a type of nonlinear programming.
Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.
Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard. With recent advancements in computing and optimization algorithms, convex programming is nearly as straightforward as linear programming.
John Emory Dennis, Jr. is an American mathematician who has made major contributions in mathematical optimization. Dennis is currently a Noah Harding professor emeritus and research professor in the department of computational and applied mathematics at Rice University in Houston, Texas. His research interests include optimization in engineering design. He is the founder and editor-in-chief of the SIAM Journal on Optimization. In 2010, he was elected a Fellow of the Society for Industrial and Applied Mathematics.
In convex analysis and the calculus of variations, both branches of mathematics, a pseudoconvex function is a function that behaves like a convex function with respect to finding its local minima, but need not actually be convex. Informally, a differentiable function is pseudoconvex if it is increasing in any direction where it has a positive directional derivative. The property must hold in all of the function domain, and not only for nearby points.
A set-valued function is a mathematical function that maps elements from one set, the domain of the function, to subsets of another set. Set-valued functions are used in a variety of mathematical fields, including optimization, control theory and game theory.
Dimitri Panteli Bertsekas is an applied mathematician, electrical engineer, and computer scientist, a McAfee Professor at the Department of Electrical Engineering and Computer Science in School of Engineering at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, and also a Fulton Professor of Computational Decision Making at Arizona State University, Tempe.
The scenario approach or scenario optimization approach is a technique for obtaining solutions to robust optimization and chance-constrained optimization problems based on a sample of the constraints. It also relates to inductive reasoning in modeling and decision-making. The technique has existed for decades as a heuristic approach and has more recently been given a systematic theoretical foundation.
Ralph Tyrrell Rockafellar is an American mathematician and one of the leading scholars in optimization theory and related fields of analysis and combinatorics. He is the author of four major books including the landmark text "Convex Analysis" (1970), which has been cited more than 27,000 times according to Google Scholar and remains the standard reference on the subject, and "Variational Analysis" for which the authors received the Frederick W. Lanchester Prize from the Institute for Operations Research and the Management Sciences (INFORMS).
In mathematics, the spectral abscissa of a matrix or a bounded linear operator is the greatest real part of the matrix's spectrum. It is sometimes denoted . As a transformation :\mathrm {M} ^{n}\rightarrow \mathbb {R} } , the spectral abscissa maps a square matrix onto its largest real eigenvalue.
Andrzej Piotr Ruszczyński is a Polish-American applied mathematician, noted for his contributions to mathematical optimization, in particular, stochastic programming and risk-averse optimization.
Yurii Nesterov is a Russian mathematician, an internationally recognized expert in convex optimization, especially in the development of efficient algorithms and numerical optimization analysis. He is currently a professor at the University of Louvain (UCLouvain).
Alfredo Noel Iusem is an Argentine-born Brazilian mathematician working on mathematical optimization.
Peter Richtarik is a Slovak mathematician and computer scientist working in the area of big data optimization and machine learning, known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning. He is currently a Professor of Computer Science at the King Abdullah University of Science and Technology.
Frank "Francis" H. Clarke is a Canadian and French mathematician.
Panagiotis E. Souganidis is an American mathematician, specializing in partial differential equations.
A multivariate polynomial is SOS-convex (or sum of squares convex) if its Hessian matrix H can be factored as H(x) = ST(x)S(x) where S is a matrix (possibly rectangular) which entries are polynomials in x. In other words, the Hessian matrix is a SOS matrix polynomial.
Guillaume Carlier is a French mathematician. Most of his work lies in the field of calculus of variation and optimization. He is a professor of applied mathematics at Paris Dauphine University and a researcher at Mokaplan, a joint INRIA-CNRS-Université Paris-Dauphine team dedicated to research in the field of optimal transport.
James Milton Renegar Jr. is an American mathematician, specializing in optimization algorithms for linear programming and nonlinear programming.
Werner Römisch is a German mathematician, professor emeritus at the Humboldt University of Berlin, most known for his pioneer work in the field of stochastic programming.
