Optimal endpoints

A complete set of necessary and sufficient conditions for selecting optimal endpoints for extremals obtained from the variational Bolza problem in control notation has been developed. The method used to obtain these conditions is based on a seldom used concept of performing a dichotomy on the general optimization problem. With this concept, the problem of Bolza is decomposed into two problems, the first of which involves the selection of optimal paths with the endpoints considered fixed. The second problem involves the selection of optimal endpoints with the paths between the endpoints taken to be stationary curves. The convenience of the dichotomy in deriving the necessary and sufficient conditions for endpoints lies in its simplicity and elementary character; well-known necessary and sufficient conditions from the theory of ordinary maxima and minima are used.An endpoint necessary condition is first obtained which is simply the well-known transversality condition. An additional condition is then developed which, together with the transversality condition, leads to a set of necessary and sufficient conditions for a given extremal to be locally optimal with respect to endpoint variations. While the second condition presented is akin to the classical focal-point condition, the result is new in form and is directly applicable to the optimal control problem. In addition, it is relatively simple to apply and is easy to implement numerically when an analytical solution is not possible. It should be useful in situations where the transversality conditions yield more than one choice for an optimal endpoint.An analytic solution for a simple geodetics problem is presented to illustrate the theory. A discussion of numerical implementation of the sufficiency conditions and its application to an orbit transfer example is also included.This work was supported in part by the National Aeronautics and Space Administration, Grant No. NGR-03-002-001.     

Optimal partitions

Consider a set of numbersZ={z ₁≥z ₂≥...≥z _n} and a functionf defined on subsets ofZ. LetP be a partition ofZ into disjoint subsetsS _i, say,g of them. The cost ofP is defined as $$C(P) = \sum\limits_{i = 1}^g {f(S_i )} .$$ By definition, in anordered partition, every pair of subsets has the property that the numbers in one subset are all greater than or equal to every number in the other subset. The problem of minimizingC(P) over all ordered partitions is called the optimal ordered partition problem. While no efficient method is known for solving the general optimal partition problem, the optimal ordered partition problem can be solved in quadratic time by dynamic programming. In this paper, we study the conditions onf under which an optimal ordered partition is indeed an optimal partition. In particular, we present an additive model and a multiplicative model for the functionf and give conditions such that the optimal partition problem can be reduced to the optimal ordered partition problem. We illustrate our results by applying them on problems which have been investigated previously in the literature.     

Optimal constrained bidding

Consider a bidder who, subject only to a constraint on his choice of bidding strategy, will choose a strategy that maximizes his expected gross receipts. The bidder's choice of strategy may be constrained by an upper limit on exposure, an upper limit on expected expenditures, or a lower limit on expected profits. We prove that constraining the bidder only by an appropriate upper limit on expected expenditures in effect limits him to expected profit maximizing strategies; in many examples, expected profit maximizing strategies can not be induced by solely limiting the bidder's exposure.     

Optimal Pricing for Optimal Transport

Suppose that c(x, y) is the cost of transporting a unit of mass from x ∈ X to y ∈ Y and suppose that a mass distribution μ on X is transported optimally (so that the total cost of transportation is minimal) to the mass distribution ν on Y. Then, roughly speaking, the Kantorovich duality theorem asserts that there is a price f(x) for a unit of mass sold (say by the producer to the distributor) at x and a price g(y) for a unit of mass sold (say by the distributor to the end consumer) at y such that for any x ∈ X and y ∈ Y, the price difference g(y) ? f(x) is not greater than the cost of transportation c(x, y) and such that there is equality g(y) ? f(x) = c(x, y) if indeed a nonzero mass was transported (via the optimal transportation plan) from x to y. We consider the following optimal pricing problem: suppose that a new pricing policy is to be determined while keeping a part of the optimal transportation plan fixed and, in addition, some prices at the sources of this part are also kept fixed. From the producers’ side, what would then be the highest compatible pricing policy possible? From the consumers’ side, what would then be the lowest compatible pricing policy possible? We have recently introduced and studied settings in c-convexity theory which gave rise to families of c-convex c-antiderivatives, and, in particular, we established the existence of optimal c-convex c-antiderivatives and explicit constructions of these optimizers were presented. In applications, it has turned out that this is a unifying language for phenomena in analysis which used to be considered quite apart. In the present paper we employ optimal c-convex c-antiderivatives and conclude that these are natural solutions to the optimal pricing problems mentioned above. This type of problems drew attention in the past and existence results were previously established in the case where X = Y = ? ⁿ under various specifications. We solve the above problem for general spaces X, Y and real-valued, lower semicontinuous cost functions c. Furthermore, an explicit construction of solutions to the general problem is presented.     

Optimal sharing

We investigate a sharing method to distribute a given quantity of resources equitably through a capacity-constrained distribution network. The sharing, called an optimal sharing, not only maximizes the minimum share but also minimizes the maximum share. The optimal sharing is obtained in time O(|T| c(n e)) where |T| is the number of sinks in the network andc(n, e) is the time required to solve the maximum flow problem.     

Optimal partial-match retrieval

This paper studies the design of a system to handle partial-match queries from a file. A partial-match query is a specification of the value of zero or more fields in a record. An answer to a query consists of a listing of all records in the file satisfying the values specified.Aho and Ullman have considered the case when the probability that a field is specified in a query is independent of which other fields are specified. We consider here the more realistic case where the independence assumption is dropped. This leads to an optimization problem more general than that considered by Aho and Ullman. The major part of the paper is the presentation of an efficient algorithm for solving this optimization problem.     

Optimal Delaunay Triangulations

The Delaunay triangulation, in both classic and more generalized sense, is studied in this paper for minimizing the linear interpolation error (measure in L^P-norm) for a given function. The classic Delaunay triangulation can then be characterized as an optimal triangulation that minimizes the interpolation error for the isotropic function ‖x‖^2 among all the triangulations with a given set of vertices. For a more general function, a functiondependent Delaunay triangulation is then defined to be an optimal triangulation that minimizes the interpolation error for this function and its construction can be obtained by a simple lifting and projection procedure. The optimal Delaunay triangulation is the one that minimizes the interpolation error among all triangulations with the same number of vertices, i.e. the distribution of vertices are optimized in order to minimize the interpolation error. Such a function-depend entoptimal Delaunay triangulation is proved to exist for any given convex continuous function.On an optimal Delaunay triangulation associated with f, it is proved that △↓f at the interior vertices can be exactly recovered by the function values on its neighboring vertices.Since the optimal Delaunay triangulation is difficult to obtain in practice, the concept of nearly optimal triangulation is introduced and two sufficient conditions are presented for a triangulation to be nearly optimal.     

Optimal p--cyclic SOR

Summary. Our purpose in this paper is to extend --cyclic SOR theory to consistent singular systems and to apply the results to the solution of large scale systems arising, {\em e.g.,} in queueing network problems in Markov analysis. Markov chains and queueing models lead to structured singular linear systems and are playing an increasing role in the understanding of complex phenomena arising in computer, communication and transportation systems. For certain important classes of singular problems, we develop a convergence theory for --cyclic SOR, and show how to repartition for optimal convergence. Results by Kontovasilis, Plemmons and Stewart on the concept of convergence of SOR in an {\em extended} sense are rigorously analyzed and applied to the solution of periodic Markov chains with period . Received October 20, 1992 / Revised version received September 14, 1993     

Almost Optimal Dispersers

A disperser is a bipartite graph with the property that every subset A of of cardinality at least K, has at least fraction of the vertices of as neighbors. Such graphs have many applications in derandomization. Saks, Srinivasan and Zhou presented an explicit construction of a disperser with an almost optimal degree , for every . We extend their result for every parameter . Received November 12, 1998/Revised June 20, 2000 RID="*" ID="*" This work was partially done while the author was at the Department of Computer Science, Weizmann Institute of Science, Rehovot, Israel. This work was supported by a Phil Zacharia postdoctoral fellowship.     

Optimal popular matchings

In this paper we consider the problem of computing an “optimal” popular matching. We assume that our input instance admits a popular matching and here we are asked to return not any popular matching but an optimal popular matching, where the definition of optimality is given as a part of the problem statement; for instance, optimality could be fairness in which case we are required to return a fair popular matching. We show an O(n²+m) algorithm for this problem, assuming that the preference lists are strict, where m is the number of edges in G and n is the number of applicants.     

Optimal key systems

A key system is represented by an ordered rooted tree. We construct trees minimizing relevant cost functions. Our application concerns interactive voice response (IVR), or automated telephone operators, for navigation purposes.     

Optimal rank-sparsity decomposition

We describe a branch-and-bound (b&b) method aimed at searching for an exact solution of the fundamental problem of decomposing a matrix into the sum of a sparse matrix and a low-rank matrix. Previous heuristic techniques employed convex and nonconvex optimization. We leverage and extend those ideas, within a spatial b&b framework, aimed at exact global optimization. Our work may serve to (i) gather evidence to assess the true quality of the previous heuristic techniques, and (ii) provide software to routinely calculate global optima or at least better solutions for moderate-sized instances coming from applications.     

Optimal discrete-valued control computation

In this paper, we consider an optimal control problem in which the control takes values from a discrete set and the state and control are subject to continuous inequality constraints. By introducing auxiliary controls and applying a time-scaling transformation, we transform this optimal control problem into an equivalent problem subject to additional linear and quadratic constraints. The feasible region defined by these additional constraints is disconnected, and thus standard optimization methods struggle to handle these constraints. We introduce a novel exact penalty function to penalize constraint violations, and then append this penalty function to the objective. This leads to an approximate optimal control problem that can be solved using standard software packages such as MISER. Convergence results show that when the penalty parameter is sufficiently large, any local solution of the approximate problem is also a local solution of the original problem. We conclude the paper with some numerical results for two difficult train control problems.     

Optimal subcategories and Stone-Weierstrass

We introduce the concept of optimal subcategory (an abstract form of completeness) and use it to develop a categorical version of the Stone-Weierstrass theorem.     

Optimal rectangle packing

We consider the NP-complete problem of finding an enclosing rectangle of minimum area that will contain a given a set of rectangles. We present two different constraint-satisfaction formulations of this problem. The first searches a space of absolute placements of rectangles in the enclosing rectangle, while the other searches a space of relative placements between pairs of rectangles. Both approaches dramatically outperform previous approaches to optimal rectangle packing. For problems where the rectangle dimensions have low precision, such as small integers, absolute placement is generally more efficient, whereas for rectangles with high-precision dimensions, relative placement will be more effective. In two sets of experiments, we find both the smallest rectangles and squares that can contain the set of squares of size 1×1, 2×2,…,N×N, for N up to 27. In addition, we solve an open problem dating to 1966, concerning packing the set of consecutive squares up to 24×24 in a square of size 70×70. Finally, we find the smallest enclosing rectangles that can contain a set of unoriented rectangles of size 1×2, 2×3, 3×4,…,N×(N+1), for N up to 25.     

最佳费用流

建立赋模糊数为费用权的容量－－费用网络中，据模糊决策来求解最佳费用流的网络模型，并给出这一模型的相应算法。     

Optimal Strategy with One Closing Instant for a Linear Optimal Guaranteed Control Problem

We consider an optimal guaranteed control problem for a linear time-varying system that is subject to unknown bounded disturbances. A control strategy is defined that guarantees steering the system to a given terminal set for any realization of disturbances and takes into account that at one future time instant the control loop will be closed. An efficient method for constructing the optimal control strategy and an algorithm for optimal feedback control based on this type of strategies are proposed.     

Optimal matrix-segmentation by rectangles

We study the problem of decomposing a nonnegative matrix into a nonnegative combination of 0-1-matrices whose ones form a rectangle such that the sum of the coefficients is minimal. We present for the case of two rows an easy algorithm that provides an optimal solution which is integral if the given matrix is integral. An additional integrality constraint makes the problem more difficult if the matrix has more than two rows. An algorithm that is based on the revised simplex method and uses only very few Gomory cuts yields exact integral solutions for integral matrices of reasonable size in a short time. For matrices of large dimension we propose a special greedy algorithm that provides sufficiently good results in numerical experiments.     

Optimal centered forms

A simple expression for an optimal center of a centered form is presented. Among all possible centers within a given interval this center yields the greatest lower bound or the lowest upper bound of a centered form, respectively. It is also shown that one-sided isotonicity holds for such centered forms.     

On the Existence of Optimal Solutions to an Optimal Control Problem

In this paper, some results concerning the existence of optimal solutions to an optimal control problem are derived. The problem involves a quasilinear hyperbolic differential equation with boundary condition and a nonlinear integral functional of action. The assumption of convexity, under which the main theorem is proved, is not connected directly with the convexity of the functional of action. In the proof, the implicit function theorem for multimappings is used.Communicated by L. D. Berkovitz