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1550 results in Control systems and optimization

Page 1 of 78

Control Systems Theory
  • N Sukavanam
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    01 May 2027

Introduction to Online Control
  • Elad Hazan, Karan Singh
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • This tutorial guide introduces online nonstochastic control, an emerging paradigm in control of dynamical systems and differentiable reinforcement learning that applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control. In optimal control, robust control, and other control methodologies that assume stochastic noise, the goal is to perform comparably to an offline optimal strategy. In online control, both cost functions and perturbations from the assumed dynamical model are chosen by an adversary. Thus, the optimal policy is not defined a priori and the goal is to attain low regret against the best policy in hindsight from a benchmark class of policies. The resulting methods are based on iterative mathematical optimization algorithms and are accompanied by finite-time regret and computational complexity guarantees. This book is ideal for graduate students and researchers interested in bridging classical control theory and modern machine learning.

Essential Mathematics for Convex Optimization
  • Fatma Kılınç-Karzan, Arkadi Nemirovski
  • Published online:
    22 October 2025
    Print publication:
    26 June 2025
  • With an emphasis on timeless essential mathematical background for optimization, this textbook provides a comprehensive and accessible introduction to convex optimization for students in applied mathematics, computer science, and engineering. Authored by two influential researchers, the book covers both convex analysis basics and modern topics such as conic programming, conic representations of convex sets, and cone-constrained convex problems, providing readers with a solid, up-to-date understanding of the field. By excluding modeling and algorithms, the authors are able to discuss the theoretical aspects in greater depth. Over 170 in-depth exercises provide hands-on experience with the theory, while more than 30 'Facts' and their accompanying proofs enhance approachability. Instructors will appreciate the appendices that cover all necessary background and the instructors-only solutions manual provided online. By the end of the book, readers will be well equipped to engage with state-of-the-art developments in optimization and its applications in decision-making and engineering.

  • 19 - Optimality Conditions in Convex Programming

  • from Part IV - Convex Programming, Lagrange Duality, Saddle Points
  • Fatma Kılınç-Karzan, Carnegie Mellon University, Pennsylvania, Arkadi Nemirovski, Georgia Institute of Technology
  • Book:
    Essential Mathematics for Convex Optimization
    Published online:
    22 October 2025
    Print publication:
    26 June 2025, pp 264-276

  • Summary

    In this chapter we present convex programming optimality conditions in both sadde point form and Karush--Kuhn--Tucker form for mathematical programming, and also optimality conditions for cone-constrained convex programs and for conic problems. We conclude the chapter by revisiting linear programming duality as a special case of conic duality and reproducing the classical results on the dual of a linearly constrained convex quadratic minimization problem.

  • 20 - ★ Cone-Convex Functions: Elementary Calculus and Examples

  • from Part IV - Convex Programming, Lagrange Duality, Saddle Points
  • Fatma Kılınç-Karzan, Carnegie Mellon University, Pennsylvania, Arkadi Nemirovski, Georgia Institute of Technology
  • Book:
    Essential Mathematics for Convex Optimization
    Published online:
    22 October 2025
    Print publication:
    26 June 2025, pp 277-283

  • Summary

    In this chapter, we (a) present epigraph characterization of cone-convexity, (b) introduce cone-monotonicity, and describe differential criteria of cone-convexity and cone-monotonicity, (c) present instructive examples of cone-convex and cone-monotone functions, (d) outline basic operations preserving cone-convexity and cone-monotonicity. Taken together, (b)--(d) provide simple and powerful tools allowing one to detect and utilize cone-convexity and cone-monotonicity.

Page 1 of 78