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The development of sophisticated experimental means to control nanoscale systems has motivated efforts to design driving protocols which minimize the energy dissipated to the environment. Computational models are a crucial tool in this…

Statistical Mechanics · Physics 2016-09-14 Todd R. Gingrich , Grant M. Rotskoff , Gavin E. Crooks , Phillip L. Geissler

The investigation of optimal processes has a long history in the field of thermodynamics. It is well known that finite-time processes that minimize dissipation often exhibit discontinuities. We use a combination of numerical and analytical…

Statistical Mechanics · Physics 2022-05-31 Ilana Bogod , Saar Rahav

Run-and-tumble particles constitute one of the simplest models of self-propelled active matter, and provide an ideal playground to the understanding of out-of-equilibrium systems. We consider an idealized setup where one such particle is…

Statistical Mechanics · Physics 2026-02-05 Marco Baldovin , Alessandro Manacorda

We review recent progress in optimal control in stochastic thermodynamics. Theoretical advances provide in-depth insight into minimum-dissipation control with either full or limited (parametric) control, and spanning the limits from slow to…

Statistical Mechanics · Physics 2023-04-13 Steven Blaber , David A. Sivak

We study thermodynamic processes in contact with a heat bath that may have an arbitrary time-varying periodic temperature profile. Within the framework of stochastic thermodynamics, and for models of thermo-dynamic engines in the idealized…

Optimization and Control · Mathematics 2022-04-06 Olga Movilla Miangolarra , Rui Fu , Amirhossein Taghvaei , Yongxin Chen , Tryphon T. Georgiou

We perform an analytic study on the stochastic thermodynamics of a small classical particle trapped in a time dependent single-well potential in the highly underdamped limit. It is shown that the nonequilibrium probability density function…

Statistical Mechanics · Physics 2019-06-26 Domingos S. P. Salazar , Sérgio A. Lira

Performing thermodynamic tasks within finite time while minimizing thermodynamic costs is a central challenge in stochastic thermodynamics. Here, we develop a unified framework for optimizing the thermodynamic cost of performing various…

Statistical Mechanics · Physics 2025-09-16 Kaito Tojo , Rihito Nagase , Ken Funo , Takahiro Sagawa

We study the dynamics of parallel tempering simulations, also known as the replica exchange technique, which has become the method of choice for simulation of proteins and other complex systems. Recent results for the optimal choice of the…

Statistical Mechanics · Physics 2009-11-13 Walter Nadler , Ulrich H. E. Hansmann

We study scheduling problems motivated by recently developed techniques for microprocessor thermal management at the operating systems level. The general scenario can be described as follows. The microprocessor's temperature is controlled…

Data Structures and Algorithms · Computer Science 2008-01-29 Marek Chrobak , Christoph Durr , Mathilde Hurand , Julien Robert

An important class of dynamical systems with several practical applications is linear systems with quadratic outputs. These models have the same state equation as standard linear time-invariant systems but differ in their output equations,…

Systems and Control · Electrical Eng. & Systems 2024-08-13 Umair Zulfiqar , Zhi-Hua Xiao , Qiu-Yan Song , Mohammad Monir Uddin , Victor Sreeram

What are the fundamental limitations for finite-time engines that extract work from active nonequilibrium systems, and what are the optimal protocols that approach them? We show that the finite-time work extraction for nonconservative…

Statistical Mechanics · Physics 2025-12-22 Adrianne Zhong , Adam G. Frim , Michael R. DeWeese

The goal of thermodynamic optimal control theory is to find protocols to change the state of a system from an initial to a desired final distribution, within a finite time, with the least possible expenditure of work. The optimal protocol…

Statistical Mechanics · Physics 2025-12-18 Daan Mulder , Thomas E. Ouldridge , Pieter Rein ten Wolde

Optimal control theory deals with finding protocols to steer a system between assigned initial and final states, such that a trajectory-dependent cost function is minimized. The application of optimal control to stochastic systems is an…

Statistical Mechanics · Physics 2024-09-18 Julia Sanders , Marco Baldovin , Paolo Muratore-Ginanneschi

Annealing algorithms such as simulated annealing and population annealing are widely used both for sampling the Gibbs distribution and solving optimization problems (i.e. finding ground states). For both statistical mechanics and…

Statistical Mechanics · Physics 2024-05-13 Amin Barzegar , Firas Hamze , Christopher Amey , Jonathan Machta

We propose a reformulation of the problem of optimally controlled transitions in stochastic thermodynamics. We impose that any terminal cost specified by a thermodynamic functional should depend only on state variables and not on control…

Statistical Mechanics · Physics 2025-10-03 Julia Sanders , Paolo Muratore-Ginanneschi

We optimize finite-time stochastic heat engines with a periodically scaled Hamiltonian under experimentally motivated constraints on the bath temperature $T$ and the scaling parameter $\lambda$. We present a general geometric proof that…

Statistical Mechanics · Physics 2022-11-28 Zhuolin Ye , Federico Cerisola , Paolo Abiuso , Janet Anders , Martí Perarnau-Llobet , Viktor Holubec

Knowing if an optimal solution is local or global has always been a hard question to answer in more sophisticated situations of optimization problems. In this work, for finite-time and weak isothermal driving processes, we show the…

Statistical Mechanics · Physics 2023-02-16 Pierre Nazé

The optimal control of passive systems in equilibrium typically favours quasistatic (infinite-time) protocols. We show that a breakdown of quasistatic optimality occurs when the controller itself is dissipative. Concretely, we study a…

Statistical Mechanics · Physics 2026-05-08 Luca Cocconi , Henry Alston , Thibault Bertrand

Parallelization is a popular strategy for improving the performance of iterative algorithms. Optimization methods are no exception: design of efficient parallel optimization methods and tight analysis of their theoretical properties are…

Optimization and Control · Mathematics 2023-11-28 Alexander Tyurin , Peter Richtárik

When engineering microscopic machines, increasing efficiency can often come at a price of reduced reliability due to the impact of stochastic fluctuations. Here we develop a general method for performing multi-objective optimisation of…

Quantum Physics · Physics 2021-01-04 Harry J. D. Miller , Mohammad Mehboudi