Finite Number Of Linear Constraints Research Articles

Individual and cooperative portfolio optimization as linear program

We derive a linear program for minimization, subject to a linear constraint, of an arbitrary positively homogeneous convex functional, whose dual set is given by linear inequalities, possibly involving auxiliary variables. This allows to reduce to linear programming individual and cooperative portfolio optimization problems with arbitrary deviation measures whose risk envelopes are given by a finite number of linear constraints. Earlier, such linear programs were known only for individual porfolio optimization problems with special examples of deviation measures, such as mean absolute deviation or CVaR deviation.

Szegő and Widom Theorems for Finite Codimensional Subalgebras of a Class of Uniform Algebras

We establish versions of Szeg\H{o}'s distance formula and Widom's theorem on invertibility of (a family of) Toeplitz operators in a class of finite codimension subalgebras of uniform algebras, obtained by imposing a finite number of linear constraints. Each such algebra is naturally represented on a family of reproducing kernel Hilbert spaces, which play a central role in the proofs.

Characterizing coherence, correcting incoherence

Lower previsions defined on a finite set of gambles can be looked at as points in a finite-dimensional real vector space. Within that vector space, the sets of sure loss avoiding and coherent lower previsions form convex polyhedra. We present procedures for obtaining characterizations of these polyhedra in terms of a minimal, finite number of linear constraints. As compared to the previously known procedure, these procedures are more efficient and much more straightforward. Next, we take a look at a procedure for correcting incoherent lower previsions based on pointwise dominance. This procedure can be formulated as a multi-objective linear program, and the availability of the finite characterizations provide an avenue for making these programs computationally feasible.

Matrix-Based Algorithms for Constrained Least-Squares and Minimax Designs of 2-D Linear-Phase FIR Filters

The impulse response coefficients of a two-dimensional (2-D) finite impulse response (FIR) filter are in a matrix form in nature. Conventional optimal design algorithms rearrange the filter's coefficient matrix into a vector and then solve for the coefficient vector using design algorithms for one-dimensional (1-D) FIR filters. Some recent design algorithms have exploited the matrix nature of the 2-D filter's coefficients but not incorporated with any constraints, and thus are not applicable to the design of 2-D filters with explicit magnitude constraints. In this paper, we develop some efficient algorithms exploiting the coefficients' matrix nature for the constrained least-squares (CLS) and minimax designs of quadrantally symmetric 2-D linear-phase FIR filters, both of which can be formulated as an optimization problem or converted into a sequence of subproblems with a positive-definite quadratic cost and a finite number of linear constraints expressed in terms of the filter's coefficient matrix. Design examples and comparisons with several existing algorithms demonstrate the effectiveness and efficiency of the proposed algorithms.

Optimal Path Problems with Second-Order Stochastic Dominance Constraints

This paper studies optimal path problems integrated with the concept of second order stochastic dominance. These problems arise from applications where travelers are concerned with the trade off between the risks associated with random travel time and other travel costs. Risk-averse behavior is embedded by requiring the random travel times on the optimal paths to stochastically dominate that on a benchmark path in the second order. A general linear operating cost is introduced to combine link- and path-based costs. The latter, which is the focus of the paper, is employed to address schedule costs pertinent to late and early arrival. An equivalent integer program to the problem is constructed by transforming the stochastic dominance constraint into a finite number of linear constraints. The problem is solved using both off-the-shelf solvers and specialized algorithms based on dynamic programming (DP). Although neither approach ensures satisfactory performance for general large-scale problems, the numerical experiments indicate that the DP-based approach provides a computationally feasible option to solve medium-size instances (networks with several thousand links) when correlations among random link travel times can be ignored.

MIMO Transceiver Optimization With Linear Constraints on Transmitted Signal Covariance Components

This correspondence revisits the joint transceiver optimization problem for multiple-input multiple-output (MIMO) channels. The linear transceiver as well as the transceiver with linear precoding and decision feedback equalization are considered. For both types of transceivers, in addition to the usual total power constraint, an individual power constraint on each antenna element is also imposed. A number of objective functions including the average bit error rate, are considered for both of the above systems under the generalized power constraint. It is shown that for both types of systems the optimization problem can be solved by first solving a class of MMSE problems (AM-MMSE or GM-MMSE depending on the type of transceiver), and then using majorization theory. The first step, under the generalized power constraint, can be formulated as a semidefinite program (SDP) for both types of transceivers, and can be solved efficiently by convex optimization tools. The second step is addressed by using results from majorization theory. The framework developed here is general enough to add any finite number of linear constraints to the covariance matrix of the input.

Farkas' Lemma, other theorems of the alternative, and linear programming in infinite-dimensional spaces: a purely linear-algebraic approach

We briefly consider several formulations of Farkas' Lemma first. Then we assume the setting of two vector spaces, one of them being linearly ordered, over a linearly ordered field till the end of this article. In this setting, we state a generalized version of Farkas' Lemma and prove it in a purely linear-algebraic way. Afterwards, we present Theorems of Motzkin, Tucker, Carver, Dax, and some other theorems of the alternative that characterize consistency of a finite system of linear inequalities. We also mention the Key Theorem, which is a related result. Finally, we use Farkas' Lemma to prove the Duality Theorem for linear programming (with a finite number of linear constraints). The Duality Theorem that is proved here covers, among others, linear programming in a real vector space of finite or infinite dimension and lexicographic linear programming.

Constructing a monotonic quadratic objective function in n variables from a few two-dimensional indifferences

We develop a model for constructing quadratic objective functions in n target variables. At the input, a decision maker is asked a few simple questions about his ordinal preferences (comparing two-dimensional alternatives in terms `better', `worse', `indifferent'). At the output, the model mathematically derives a quadratic objective function used to evaluate n-dimensional alternatives. Thus the model deals with some imaginary decisions (criteria aggregates) at the input, and disaggregates the decision maker's preference into partial criteria and their cross-correlations (=a quadratic objective function). Therefore, the model provides an approximation step which is next to the disaggregation of a preference into additively separable linear criteria with weight coefficients. The model is based on least squares fitting a quadratic indifference hypersurface (if n=2, indifference curve) to several alternatives which are supposed to be equivalent in preference. The resulting ordinal preference is independent of the cardinal utility scale used in intermediate computations which implies that the model is ordinal. The monotonicity of the quadratic objective function is implemented by means of a finite number of linear constraints, so that the computational model is reduced to restricted least squares. In illustration, we construct a quadratic objective function of German economic policy in four target variables: inflation, unemployment, GNP growth, and increase in public debt. This objective function is used to evaluate the German economic development in 1980–1994. In another application, we construct a quadratic objective function of ski station customers. Then it is used to adjust prices of 10 ski stations to the South of Stuttgart. In Appendix A we provide an original fast algorithm for restricted least squares and quadratic programming used in the main model.

ℓ1-Minimization with Magnitude Constraints in the Frequency Domain

In this paper, we study the \(\ell _1 \)-optimal control problem with additional constraints on the magnitude of the closed-loop frequency response. In particular, we study the case of magnitude constraints at fixed frequency points (a finite number of such constraints can be used to approximate an \(H_\infty \)-norm constraint). In previous work, we have shown that the primal-dual formulation for this problem has no duality gap and both primal and dual problems are equivalent to convex, possibly infinite-dimensional, optimization problems with LMI constraints. Here, we study the effect of approximating the convex magnitude constraints with a finite number of linear constraints and provide a bound on the accuracy of the approximation. The resulting problems are linear programs. In the one-block case, both primal and dual programs are semi-infinite dimensional. The optimal cost can be approximated, arbitrarily well from above and within any predefined accuracy from below, by the solutions of finite-dimensional linear programs. In the multiblock case, the approximate LP problem (as well as the exact LMI problem) is infinite-dimensional in both the variables and the constraints. We show that the standard finite-dimensional approximation method, based on approximating the dual linear programming problem by sequences of finite-support problems, may fail to converge to the optimal cost of the infinite-dimensional problem.

Entropy Minimization with Lattice Bounds

We characterize solutions to the problem of minimizing a convex integral objective function subject to a finite number of linear constraints and requiring that the feasible functions lie in a strip [α,β] where α and β are extended real valued measurable functions. We use the duality theory of J. M. Borwein and A. S. Lewis ( Math. Programming, Series B 57 (1992), 15-48, 49-84) to show that the solutions are of the usual form, but truncated where they leave the strip.

Maximisation and minimisation on classes of rearrangements

SynopsisMaximisation and minimisation of the Dirichlet integral of a function vanishing on the boundary of a bounded domain are studied, subject to the constraint that the Laplacean be a rearrangement of a given function. When the Laplacean is two-signed, non-existence of minimisers is proved, and some information on the limits of minimising sequences obtained; this contrasts with the known existence of minimisers in the one-signed case. When the domain is a ball and the Laplacean is one-signed, maximisers and minimisers are shown to be radial and monotone. Existence of maximisers is proved subject additionally to a finite number of linear constraints, with particular reference to ideal fluid flows of prescribed angular momentum in a disc.

ϵ-Subgradient projection algorithm

Using only certain easily computable ε-subgradients, an implementable convergent algorithm for finding the minimizer of a non-differentiable objective function subject to a finite number of linear constraints in d-dimensional space is given. The particular objective function consists of the pointwise maximum of a finite system of pseudoconvex functions. At each iteration cycle certain projections are computed. The negatives of these directions are feasible directions of strict descent for the objective function. The convergence of the algorithm is proved. The algorithm has also been numerically tested.

A method for the determination of absolute geostrophic velocities in the sea

A method utilizing hydrographic data and vertically-averaged currents for the determination of absolute geostrophic velocities in the sea is described. Relative dynamic depths are interpolated on a selected grid of points by (i) using the linear least-squares estimation for a given function with “unknown data” and (ii) solving the Poisson equation with Dirichlet boundary conditions and a finite number of linear constraints within the domain investigated. Once the fields of relative velocities are computed and the measured vertically-averaged currents are interpolated on the grid using the same procedure as for the relative dynamic depths, the absolute currents may be calculated by simply subtracting the vertically-averaged relative velocities from the vertical averages of the currents. This method is illustrated by an example from the Rijeka Bay, a small basin in the northeast Adriatic Sea. The results for the Rijeka Bay are interesting from a qualitative point of view, whereas for the exact quantitative reproduction of the Bay circulation, higher-order dynamics should be invoked. It is concluded that the method in its original form is more suitable for the open sea, and the experiment needed for its application in such an environment is briefly described. DOI: 10.1111/j.1600-0870.1987.tb00291.x

An algorithm for linearly constrained convex nondifferentiable minimization problems

A readily implementable algorithm is proposed for minimizing any convex, not necessarily differentiable, function f of several variables subject to a finite number of linear constraints. The algorithm requires only the calculation of f at designated feasible points. At each iteration a lower polyhedral approximation to f is constructed from a few previously calculated subgradients and an aggregate subgradient. The recursively updated aggregate subgradient accumulates the subgradient information to deal with nondifferentiability of f. The polyhedral approximation and the linear constraints generate constraints in the search direction finding subproblem that is a quadratic programming problem. Then a step length is found by approximate line search. It is shown that the algorithm converges to a solution, if any.