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This chapter introduces the important idea of a vector through the example of bundles of goods. The dot product of two vectors is defined and it is shown how a budget constraint can be expressed in terms of dot product. It is explained how, in order to rank bundles according to a particular consumer's preference, we can use a utility function. Indifference curves are defined as the contours of the utility function. Linear and convex combinations and the concept of a convex set are explained. The utility maximisation problem -- to maximise utility subject to a budget constraint -- is explored and the relevance of convexity is emphasised.
We resolve some questions posed by Handelman in 1996 concerning log convex $L^1$ functions. In particular, we give a negative answer to a question he posed concerning the integrability of $h^2(x)/h(2x)$ when h is $L^1$ and log convex and $h(n)^{1/n}\rightarrow 1$.
We present a detailed analysis of random motions moving in higher spaces with a natural number of velocities. In the case of the so-called minimal random dynamics, under some broad assumptions, we give the joint distribution of the position of the motion (for both the inner part and the boundary of the support) and the number of displacements performed with each velocity. Explicit results for cyclic and complete motions are derived. We establish useful relationships between motions moving in different spaces, and we derive the form of the distribution of the movements in arbitrary dimension. Finally, we investigate further properties for stochastic motions governed by non-homogeneous Poisson processes.
This paper relies on nested postulates of separate, linear and arc-continuity of functions to define analogous properties for sets that are weaker than the requirement that the set be open or closed. This allows three novel characterisations of open or closed sets under convexity or separate convexity postulates: the first pertains to separately convex sets, the second to convex sets and the third to arbitrary subsets of a finite-dimensional Euclidean space. By relying on these constructions, we also obtain new results on the relationship between separate and joint continuity of separately quasiconcave, or separately quasiconvex functions. We present examples to show that the sufficient conditions we offer cannot be dispensed with.
We introduce a family of norms on the $n \times n$ complex matrices. These norms arise from a probabilistic framework, and their construction and validation involve probability theory, partition combinatorics, and trace polynomials in noncommuting variables. As a consequence, we obtain a generalization of Hunter’s positivity theorem for the complete homogeneous symmetric polynomials.
The structure of equilibrium thermodynamics, in harmony with statistical mechanics, does not contain a time asymmetry, except when it is trivially supplemented with one.
This appendix collects a review of the calculus and analysis in one and several variables that the reader should be familiar with. Notions of convergence, continuity, differentiability and integrability are recalled here.
This chapter serves two purposes: it introduces several essential concepts of linear and nonlinear functional analysis that will be used in subsequent chapters and, as an illustration of them, studies the problem of unconstrained minimization of a convex functional. All the necessary notions of existence, uniqueness, and optimality conditions are presented and analyzed. Preconditioned gradient descent methods for strongly convex, locally Lipschitz smooth objectives in infinite dimensions are then presented and analyzed. A general framework to show linear convergence in this setting is then presented. The preconditioned steepest descent with exact and approximate line searches are then analyzed using the same framework. Finally, the application of Newton’s method to the Euler equations is discussed. The local convergence is shown, and how to achieve global convergence is briefly discussed.
The paper proves transportation inequalities for probability measures on spheres for the Wasserstein metrics with respect to cost functions that are powers of the geodesic distance. Let $\mu$ be a probability measure on the sphere ${\bf S}^n$ of the form $d\mu =e^{-U(x)}{\rm d}x$ where ${\rm d}x$ is the rotation invariant probability measure, and $(n-1)I+{\hbox {Hess}}\,U\geq {\kappa _U}I$, where $\kappa _U>0$. Then any probability measure $\nu$ of finite relative entropy with respect to $\mu$ satisfies ${\hbox {Ent}}(\nu \mid \mu ) \geq (\kappa _U/2)W_2(\nu,\, \mu )^2$. The proof uses an explicit formula for the relative entropy which is also valid on connected and compact $C^\infty$ smooth Riemannian manifolds without boundary. A variation of this entropy formula gives the Lichnérowicz integral.
A number of recent papers have estimated ratios of the partition function
$p(n-j)/p(n)$
, which appear in many applications. Here, we prove an easy-to-use effective bound on these ratios. Using this, we then study the second shifted difference of partitions,
$f(\,j,n) := p(n) -2p(n-j) +p(n-2j)$
, and give another easy-to-use estimate of
$f(\,j,n)$
. As applications of these, we prove a shifted convexity property of
$p(n)$
, as well as giving new estimates of the k-rank partition function
$N_k(m,n)$
and non-k-ary partitions along with their differences.
The practically important classes of equal-input and of monotone Markov matrices are revisited, with special focus on embeddability, infinite divisibility, and mutual relations. Several uniqueness results for the classic Markov embedding problem are obtained in the process. To achieve our results, we need to employ various algebraic and geometric tools, including commutativity, permutation invariance, and convexity. Of particular relevance in several demarcation results are Markov matrices that are idempotents.
In this chapter, we study risks associated with movements of interest rates in financial markets. We begin with a brief discussion of the term structure of interest rates. We then discuss commonly used interest rate sensitive securities. This is followed by the study of different measures of sensitivity to interest rates, including duration and convexity. We consider mitigating interest rate risk through hedging and immunization. Finally, we take a more in-depth look at the drivers of interest rate term structure dynamics.
We outline theoretical foundations for smooth optimization problems. First, we define the different types of minimizers (solutions) of unconstrained optimization problems. Next, we state Taylor’s theorem, the fundamental theorem of smooth optimization, which allows us to approximate general smooth functions by simpler (linear or quadratic) functions based on information at the current point. We show how minima can be characterized by optimality conditions involving the gradient or Hessian, which can be checked in practice. Finally, we define the convexity of sets and functions, an important property that arises often in practice and that can be exploited by the algorithms described in the remainder of the book.
This chapter first introduces basic concepts in nonlinear optimization, especially feasible regions and convexity conditions. Sufficient conditions are provided for both convex regions and convex functions. Next, optimality conditions are presented for unconstrained optimization problems (stationary conditions), and constrained problems with equality constraints (stationary condition of Lagrange function) and with inequality constraints (Fritz-John Theorem).The chapter concludes with nonlinear optimization with equality and equalityconstraints that lead to the Karush–Kuhn–Tucker conditions. Finally, an active set strategy is introduced for the solution of small nonlinear programming problems, and is illustrated with a small example.
Quadratic multidimensional functions play a very important role in the understanding of general nonlinear functions. Convexity of quadratic functions is linked in a natural way from its geometrical definition all the way to the properties of its matrix eigenspectrum.Indeed, to second order expansion, and close to the expansion point, any nonlinear function can be approximated by a quadratic – thus providing a crucial link and understanding of the local behaviour and convexity properties of general functions.
Let
$(X,T)$
be a topological dynamical system. Given a continuous vector-valued function
$F \in C(X, \mathbb {R}^{d})$
called a potential, we define its rotation set
$R(F)$
as the set of integrals of F with respect to all T-invariant probability measures, which is a convex body of
$\mathbb {R}^{d}$
. In this paper we study the geometry of rotation sets. We prove that if T is a non-uniquely ergodic topological dynamical system with a dense set of periodic measures, then the map
$R(\cdot )$
is open with respect to the uniform topologies. As a consequence, we obtain that the rotation set of a generic potential is strictly convex and has
$C^{1}$
boundary. Furthermore, we prove that the map
$R(\cdot )$
is surjective, extending a result of Kucherenko and Wolf.
For
$n\geq 3$
, let
$Q_n\subset \mathbb {C}$
be an arbitrary regular n-sided polygon. We prove that the Cauchy transform
$F_{Q_n}$
of the normalised two-dimensional Lebesgue measure on
$Q_n$
is univalent and starlike but not convex in
$\widehat {\mathbb {C}}\setminus Q_n$
.
The class of distortion riskmetrics is defined through signed Choquet integrals, and it includes many classic risk measures, deviation measures, and other functionals in the literature of finance and actuarial science. We obtain characterization, finiteness, convexity, and continuity results on general model spaces, extending various results in the existing literature on distortion risk measures and signed Choquet integrals. This paper offers a comprehensive toolkit of theoretical results on distortion riskmetrics which are ready for use in applications.
Explains the notion of discrepancy. Describesconnections to randomized communicationcomplexity. Proves lower bounds using convexityand concentration of measure.
Research on producer willingness to adopt individual best pasture management practices (BMPs) is extensive, but less attention has been paid to producers simultaneously adopting multiple, complementary BMPs. Applications linking primary survey data on BMP adoption to water quality biophysical models are also limited. A choice-experiment survey of livestock producers is analyzed to determine willingness to adopt pasture BMPs. Sediment abatement curves are derived by linking estimates of producer responsiveness to incentives to adopt rotational grazing with a biophysical simulation model. Current cost share rates of $24/acre should yield a 12% decrease in sediment loading from pastures.