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This book places thermodynamics on a system-theoretic foundation so as to harmonize it with classical mechanics. Using the highest standards of exposition and rigor, the authors develop a novel formulation of thermodynamics that can be viewed as a moderate-sized system theory as compared to statistical thermodynamics. This middle-ground theory involves deterministic large-scale dynamical system models that bridge the gap between classical and statistical thermodynamics. The authors' theory is motivated by the fact that a discipline as cardinal as thermodynamics--entrusted with some of the most perplexing secrets of our universe--demands far more than physical mathematics as its underpinning. Even though many great physicists, such as Archimedes, Newton, and Lagrange, have humbled us with their mathematically seamless eurekas over the centuries, this book suggests that a great many physicists and engineers who have developed the theory of thermodynamics seem to have forgotten that mathematics, when used rigorously, is the irrefutable pathway to truth. This book uses system theoretic ideas to bring coherence, clarity, and precision to an extremely important and poorly understood classical area of science.

Thermodynamics --- Differentiable dynamical systems. --- Differential dynamical systems --- Dynamical systems, Differentiable --- Dynamics, Differentiable --- Differential equations --- Global analysis (Mathematics) --- Topological dynamics --- Chemistry, Physical and theoretical --- Dynamics --- Mechanics --- Physics --- Heat --- Heat-engines --- Quantum theory --- Mathematics. --- Addition. --- Adiabatic process. --- Applied mathematics. --- Arthur Eddington. --- Asymmetry. --- Available energy (particle collision). --- Axiom. --- Balance equation. --- Banach space. --- Boltzmann's entropy formula. --- Brillouin scattering. --- Carnot cycle. --- Classical mechanics. --- Clausius (crater). --- Compact space. --- Conservation law. --- Conservation of energy. --- Constant of integration. --- Continuous function (set theory). --- Continuous function. --- Control theory. --- Deformation (mechanics). --- Derivative. --- Diathermal wall. --- Diffeomorphism. --- Differentiable function. --- Diffusion process. --- Dimension (vector space). --- Dimension. --- Dissipation. --- Dot product. --- Dynamical system. --- Emergence. --- Energy density. --- Energy level. --- Energy storage. --- Energy. --- Entropy. --- Equation. --- Equations of motion. --- Equilibrium point. --- Equilibrium thermodynamics. --- Equipartition theorem. --- Existential quantification. --- First law of thermodynamics. --- Hamiltonian mechanics. --- Heat capacity. --- Heat death of the universe. --- Heat flux. --- Heat transfer. --- Homeomorphism. --- Hydrogen atom. --- Ideal gas. --- Inequality (mathematics). --- Infimum and supremum. --- Infinitesimal. --- Initial condition. --- Instant. --- Internal energy. --- Irreversible process. --- Isolated system. --- Kinetic theory of gases. --- Laws of thermodynamics. --- Linear dynamical system. --- Lipschitz continuity. --- Local boundedness. --- Lyapunov function. --- Lyapunov stability. --- Mathematical optimization. --- Molecule. --- Non-equilibrium thermodynamics. --- Operator norm. --- Probability. --- Quantity. --- Reversible process (thermodynamics). --- Second law of thermodynamics. --- Semi-infinite. --- Smoothness. --- State variable. --- State-space representation. --- Statistical mechanics. --- Steady state. --- Summation. --- Supply (economics). --- Systems theory. --- Temperature. --- Theorem. --- Theoretical physics. --- Theory. --- Thermal conduction. --- Thermal equilibrium. --- Thermodynamic equilibrium. --- Thermodynamic process. --- Thermodynamic state. --- Thermodynamic system. --- Thermodynamic temperature. --- Thermodynamics. --- Time evolution. --- Zeroth law of thermodynamics.

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Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.

Robust optimization. --- Linear programming. --- 519.8 --- 681.3*G16 --- 681.3*G16 Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- 519.8 Operational research --- Operational research --- Robust optimization --- Linear programming --- Optimisation robuste --- Programmation linéaire --- Optimization, Robust --- RO (Robust optimization) --- Mathematical optimization --- Production scheduling --- Programming (Mathematics) --- 0O. --- Accuracy and precision. --- Additive model. --- Almost surely. --- Approximation algorithm. --- Approximation. --- Best, worst and average case. --- Bifurcation theory. --- Big O notation. --- Candidate solution. --- Central limit theorem. --- Chaos theory. --- Coefficient. --- Computational complexity theory. --- Constrained optimization. --- Convex hull. --- Convex optimization. --- Convex set. --- Cumulative distribution function. --- Curse of dimensionality. --- Decision problem. --- Decision rule. --- Degeneracy (mathematics). --- Diagram (category theory). --- Duality (optimization). --- Dynamic programming. --- Exponential function. --- Feasible region. --- Floor and ceiling functions. --- For All Practical Purposes. --- Free product. --- Ideal solution. --- Identity matrix. --- Inequality (mathematics). --- Infimum and supremum. --- Integer programming. --- Law of large numbers. --- Likelihood-ratio test. --- Linear dynamical system. --- Linear inequality. --- Linear map. --- Linear matrix inequality. --- Linear regression. --- Loss function. --- Margin classifier. --- Markov chain. --- Markov decision process. --- Mathematical optimization. --- Max-plus algebra. --- Maxima and minima. --- Multivariate normal distribution. --- NP-hardness. --- Norm (mathematics). --- Normal distribution. --- Optimal control. --- Optimization problem. --- Orientability. --- P versus NP problem. --- Pairwise. --- Parameter. --- Parametric family. --- Probability distribution. --- Probability. --- Proportionality (mathematics). --- Quantity. --- Random variable. --- Relative interior. --- Robust control. --- Robust decision-making. --- Semi-infinite. --- Sensitivity analysis. --- Simple set. --- Singular value. --- Skew-symmetric matrix. --- Slack variable. --- Special case. --- Spherical model. --- Spline (mathematics). --- State variable. --- Stochastic calculus. --- Stochastic control. --- Stochastic optimization. --- Stochastic programming. --- Stochastic. --- Strong duality. --- Support vector machine. --- Theorem. --- Time complexity. --- Uncertainty. --- Uniform distribution (discrete). --- Unimodality. --- Upper and lower bounds. --- Variable (mathematics). --- Virtual displacement. --- Weak duality. --- Wiener filter. --- With high probability. --- Without loss of generality.