Splitting a configuration in a simplex

This paper presents a new method of partition, named-splitting, of a point set ind-dimensional space. Given a pointG in ad-dimensional simplexT, T(G;i) is the subsimplex spanned by G and the ith facet ofT. LetS be a set ofn points inT, and let be a sequence of nonnegative integers ₁, ..., n_d+1 satisfying _i=1 ^d+1 ₁=n The-splitter of (T, S) is a pointG inT such thatT(G;i) contains at least _i points ofS in its closure for everyi=1, 2, ...,d + 1. The associated dissection is the re-splitting.The existence of a-splitting is shown for any (T, S) and, and two efficient algorithms for finding such a splitting are given. One runs inO(d²n logn + d³n) time, and the other runs inO(n) time if the dimensiond can be considered as a constant. Applications of re-splitting to mesh generation, polygonal-tour generation, and a combinatorial assignment problem are given.     

Systems of Strings with High Mutual Complexity

Consider a binary string x ₀ of Kolmogorov complexity K(x ₀) n. The question is whether there exist two strings x ₁ and x ₂ such that the approximate equalities K(x _i x _j ) n and K(x _i x _j , x _k ) n hold for all 0 i, j, k 2, i j k, i k. We prove that the answer is positive if we require the equalities to hold up to an additive term O(log K(x ₀)). It becomes negative in the case of better accuracy, namely, O(log n).     

The conjoinability relation in Lambek calculus and linear logic

In 1958 J. Lambek introduced a calculusL of syntactic types and defined an equivalence relation on types: x y means that there exists a sequence x=x₁,...,x_n=y (n 1), such thatx _i x _i+1 or x_i+ x _i (1 i n). He pointed out thatx y if and only if there is joinz such thatx z andy z. This paper gives an effective characterization of this equivalence for the Lambeck calculiL andLP, and for the multiplicative fragments of Girard's and Yetter's linear logics. Moreover, for the non-directed Lambek calculusLP and the multiplicative fragment of Girard's linear logic, we present linear time algorithms deciding whether two types are equal, and finding a join for them if they are.The author was sponsored by project NF 102/62-356 (Structural and Semantic Parallels in Natural Languages and Programming Languages), funded by the Netherlands Organization for the Advancement of Research (N.W.O.).     

Convergence of trust region augmented Lagrangian methods using variable fidelity approximation data

To date the primary focus of most constrained approximate optimization strategies is that application of the method should lead to improved designs. Few researchers have focused on the development of constrained approximate optimization strategies that are assured of converging to a Karush-Kuhn-Tucker (KKT) point for the problem. Recent work by the authors based on a trust region model management strategy has shown promise in managing the convergence of constrained approximate optimization in application to a suite of single level optimization test problems. Using a trust-region model management strategy, coupled with an augmented Lagrangian approach for constrained approximate optimization, the authors have shown in application studies that the approximate optimization process converges to a KKT point for the problem. The approximate optimization strategy sequentially builds a cumulative response surface approximation of the augmented Lagrangian which is then optimized subject to a trust region constraint. In this research the authors develop a formal proof of convergence for the response surface approximation based optimization algorithm. Previous application studies were conducted on single level optimization problems for which response surface approximations were developed using conventional statistical response sampling techniques such as central composite design to query a high fidelity model over the design space. In this research the authors extend the scope of application studies to include the class of multidisciplinary design optimization (MDO) test problems. More importantly the authors show that response surface approximations constructed from variable fidelity data generated during concurrent subspace optimization (CSSOs) can be effectively managed by the trust region model management strategy. Results for two multidisciplinary test problems are presented in which convergence to a KKT point is observed. The formal proof of convergence and the successful MDO application of the algorithm using variable fidelity data generated by CSSO are original contributions to the growing body of research in MDO.Nomenclature k Lagrangian iteration - s approximate minimization iteration - i, j, l variable indices - m number of inequality constraints - n number of design variables - p number of equality constraints - f(x) objective function - g(x) inequality constraint vector - g _j (x) j-th inequality constraint - h(x) equality constraint vector - h _j (x) i-th equality constraint - c(x) generalized constraint vector - c _i (x) i-th generalized constraint - c ₁,c ₂,c ₃,c ₄ real constants - m(x) approximate model - q(x) approximate model - q(x) piecewise approximation - r _p penalty parameter - t, t ₁,t ₂ step size length - x design vector, dimensionn - x _l l-th design variable - x ^U upper bound vector, dimensionn - x _l ^U l-th design upper bound - x ^L lower bound vector, dimensionn - x _l ^L l-th design lower bound - B approximation of the Hessian - K constraints residual - S design space - , ₁, ₂, scalars - ₁, ₂ convergence tolerances - ₀, ₁, ₂, , trust region parameters - Lagrange multiplier vector, dimensionm+p - _i i-th Lagrange multiplier - trust region ratio - (x) alternative form for inequality constraints - (x, ,r _p ) augmented Lagrangian function - approximation of the augmented Lagrangian function - fidelity control - . Euclidean norm - , inner product - gradient operator with respect to design vector x - P(y(x)) projection operator; projects the vector y onto the set of feasible directions at x - trust region radius - x step size     

Counting connected components of a semialgebraic set in subexponential time

Let a semialgebraic set be given by a quantifier-free formula of the first-order theory of real closed fields withk atomic subformulae of the typef _i0 for 1ik, where the polynomialsf _i[X ₁,...,X _n] have degrees deg(f _i)<d and the absolute value of each (integer) coefficient off _i is at most 2M. An algorithm is exhibited which counts the number of connected components of the semialgebraic set in time (M (kd)^{n 20})^{O (1)}. Moreover, the algorithm allows us to determine whether any pair of points from the set are situated in the same connected component.     

Learning criteria for training neural network classifiers

This paper presents a study of two learning criteria and two approaches to using them for training neural network classifiers, specifically a Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. The first approach, which is a traditional one, relies on the use of two popular learning criteria, i.e. learning via minimising a Mean Squared Error (MSE) function or a Cross Entropy (CE) function. It is shown that the two criteria have different charcteristics in learning speed and outlier effects, and that this approach does not necessarily result in a minimal classification error. To be suitable for classification tasks, in our second approach an empirical classification criterion is introduced for the testing process while using the MSE or CE function for the training. Experimental results on several benchmarks indicate that the second approach, compared with the first, leads to an improved generalisation performance, and that the use of the CE function, compared with the MSE function, gives a faster training speed and improved or equal generalisation performance.Nomenclature x random input vector withd real number components [x ₁ ...x _d ] - t random target vector withc binary components [t ₁ ...t _c ] - y(·) neural network function or output vector - parameters of a neural model - learning rate - momentum - decay factor - O objective function - E mean sum-of-squares error function - L cross entropy function - n nth training pattern - N number of training patterns - (·) transfer function in a neural unit - z _j output of hidden unit-j - a _i activation of unit-j - W _ij weight from hidden unit-j to output unit-i - W _jl ⁰ weight from input unit-l to hidden unit-j - _j centre vector [ _{j 1} ... _jd ] of RBF unit-j - _j width vector [ _{j 1}, ... _jd ] of RBF unit-j - p( ·¦·) conditional probability function     

Three-valued representative systems

A representative system defined onn voters or propositionsi = 1,,n is a functionF: {1,0, -1} ⁿ {1,0, -1} which is monotonic (D E F(D) F(E)), unanimous (F(1,, 1) = 1), dual (F(-D) = -F(D)), and satisfies a positivity property which says that the set of all non-zero vectors in {1, 0, -1} ⁿ for whichF(D) = 0 can be partitioned into two dual subsets each of which has the property that ifD andE are in the subset thenD _i+E _i > 0 for somei. Representative systems can be defined recursively from the coordinate projectionsS _i (D) = D _i using sign functions, and in this format they are interpreted as hierarchical voting systems in which outcomes of votes in lower levels act as votes in higher levels of the system. For each positive integern, (n) is defined as the smallest positive integer such that all representative systems defined on {1, 0, -1} ⁿ can be characterized by(n) or fewer hierarchical levels. The function is nondecreasing inn, unbounded above, and satisfies(n) n–1 for alln. In addition,(n) = n–1 forn {1, 2, 3, 4}, and it is conjectured that does not continue to grow linearly asn increases.     

A Delay-Averaged Logistic Model

For the equation x(t) = x(t) (1-(1/) _t-- ^t- x(u)du), > 0, > 0, > 0, conditions for the stability of a nonzero stationary solution under small perturbations are determined.     

Global optimization methods for engineering applications: A review

A review of the methods for global optimization reveals that most methods have been developed for unconstrained problems. They need to be extended to general constrained problems because most of the engineering applications have constraints. Some of the methods can be easily extended while others need further work. It is also possible to transform a constrained problem to an unconstrained one by using penalty or augmented Lagrangian methods and solve the problem that way. Some of the global optimization methods find all the local minimum points while others find only a few of them. In any case, all the methods require a very large number of calculations. Therefore, the computational effort to obtain a global solution is generally substantial. The methods for global optimization can be divided into two broad categories: deterministic and stochastic. Some deterministic methods are based on certain assumptions on the cost function that are not easy to check. These methods are not very useful since they are not applicable to general problems. Other deterministic methods are based on certain heuristics which may not lead to the true global solution. Several stochastic methods have been developed as some variation of the pure random search. Some methods are useful for only discrete optimization problems while others can be used for both discrete and continuous problems. Main characteristics of each method are identified and discussed. The selection of a method for a particular application depends on several attributes, such as types of design variables, whether or not all local minima are desired, and availability of gradients of all the functions.Notation Number of equality constraints - () ^T A transpose of a vector - A A hypercubic cell in clustering methods - Distance between two adjacent mesh points - Probability that a uniform sample of sizeN contains at least one point in a subsetA ofS - A(v, x) Aspiration level function - A The set of points with cost function values less thanf(x _G ^* ) +. Same asA _f () - A _f () A set of points at which the cost function value is within off(x _G ^* ) - A () A set of points x with[f(x)] smaller than - A _N The set ofN random points - A _q The set of sample points with the cost function value f _q - _Q The contraction coefficient; –1 _Q 0 - _R The expansion coefficient; _E > 1 - _R The reflection coefficient; 0 < _R 1 - A _x () A set of points that are within the distance from x _G ^* - D Diagonal form of the Hessian matrix - det() Determinant of a matrix - d _j A monotonic function of the number of failed local minimizations - d _t Infinitesimal change in time - d _x Infinitesimal change in design - A small positive constant - (t) A real function called the noise coefficient - ₀ Initial value for(t) - exp() The exponential function - f ^(c) The record; smallest cost function value over X^(C) - [f(x)] Functional for calculating the volume fraction of a subset - Second-order approximation tof(x) - f(x) The cost function - An estimate of the upper bound of global minimum - f ^E The cost function value at x^E - f ^L The cost function value at x^L - f _opt The current best minimum function value - f ^P The cost function value at x ^P - f ^Q The cost function value at x ^Q - f _q A function value used to reduce the random sample - f ^R The cost function value at x ^R - f ^S The cost function value at x^S - f _{T F min} A common minimum cost function value for several trajectories - f _{TF opt} The best current minimum value found so far forf _{TF min} - f ^W The cost function value at x ^W - G Minimum number of points in a cell (A) to be considered full - The gamma function - A factor used to scale the global optimum cost in the zooming method - Minimum distance assumed to exist between two local minimum points - gi(x) Constraints of the optimization problem - H The size of the tabu list - H(x^*) The Hessian matrix of the cost function at x^* - h _j Half side length of a hypercube - h _m Minimum half side lengths of hypercubes in one row - I The unity matrix - ILIM A limit on the number of trials before the temperature is reduced - J The set of active constraints - K Estimate of total number of local minima - k Iteration counter - The number of times a clustering algorithm is executed - L Lipschitz constant, defined in Section 2 - L The number of local searches performed - _i The corresponding pole strengths - log () The natural logarithm - LS Local search procedure - M Number of local minimum points found inL searches - m Total number of constraints - m(t) Mass of a particle as a function of time - m() TheLebesgue measure of thea set - Average cost value for a number of random sample of points inS - N The number of sample points taken from a uniform random distribution - n Number of design variables - n(t) Nonconservative resistance forces - n _c Number of cells;S is divided inton _c cells - NT Number of trajectories - Pi (3.1415926) - P _i ^(j) Hypersphere approximating thej-th cluster at stagei - p(x ⁽ⁱ⁾⁾ Boltzmann-Gibbs distribution; the probability of finding the system in a particular configuration - pg A parameter corresponding to each reduced sample point, defined in (36) - Q An orthogonal matrix used to diagonalize the Hessian matrix - _i (i = 1, K) The relative size of thei-th region of attraction - r _i ^(j) Radius of thej-th hypersp here at stagei - R _x ^* Region of attraction of a local minimum x^* - r _j Radius of a hypersphere - r A critical distance; determines whether a point is linked to a cluster - R ⁿ A set ofn tuples of real numbers - A hyper rectangle set used to approximateS - S The constraint set - A user supplied parameter used to determiner - s The number of failed local minimizations - T The tabu list - t Time - T(x) The tunneling function - T _c (x) The constrained tunneling function - T _i The temperature of a system at a configurationi - TLIMIT A lower limit for the temperature - TR A factor between 0 and 1 used to reduce the temperature - u(x) A unimodal function - V(x) The set of all feasible moves at the current design - v(x) An oscillating small perturbation. - V(y⁽ⁱ⁾) Voronoi cell of the code point y⁽ⁱ⁾ - v^–1 An inverse move - v _k A move; the change from previous to current designs - w(t) Ann-dimensional standard. Wiener process - x Design variable vector of dimensionn - x^# A movable pole used in the tunneling method - x⁽⁰⁾ A starting point for a local search procedure - X^(c) A sequence of feasible points {x⁽¹⁾, x⁽²⁾,,x^(c)} - x(t) Design vector as a function of time - X^* The set of all local minimum points - x^* A local minimum point forf(x) - x^*(i) Poles used in the tunneling method - x _G ^* A global minimum point forf(x) - Transformed design space - The velocity vector of the particle as a function of time - Acceleration vector of the particle as a function of time - x ^C Centroid of the simplex excluding x ^L - x ^c A pole point used in the tunneling method - x ^E An expansion point of x ^R along the direction x ^C x ^R - x ^L The best point of a simplex - x ^P A new trial point - x ^Q A contraction point - x ^R A reflection point; reflection of x ^W on x ^C - x ^S The second worst point of a simplex - x ^W The worst point of a simplex - The reduced sample point with the smallest function value of a full cell - Y The set of code points - y ⁽ⁱ⁾ A code point; a point that represents all the points of thei-th cell - z A random number uniformly distributed in (0,1) - Z ^(c) The set of points x where [f ^(c) – ] is smaller thanf(x) - []₊ Max (0,) - | | Absolute value - The Euclidean norm - f[x(t)] The gradient of the cost function     

Hierarchies of polynomially solvable satisfiability problems

In this paper, we introduce general techniques for extending classes of polynomially solvable SAT instances. We generalize the approach of Gallo and Scutellà, who defined the hierarchy { _i }, where _l corresponds to the Generalized Horn class. We propose a family of polynomial hierarchies, where a polynomial hierarchy { _i } is a sequence of polynomially solvable classes that cover the whole set of CNF formulas, and such that _{i i+1} fori0. Following a different approach, based on a new decomposition technique, we define the class of Split-Horn formulas, which is an extension of _l. We discuss and compare the basic properties of the proposed classes; polynomial time algorithms for recognition and solution are provided.     

Best Huffman trees

Summary Given a sequence of positive weights, W=w ₁...w _n >0, there is a Huffman tree, T (T-up) which minimizes the following functions: max{d(w_i)}; d(w_i); f(d(w_i)) w _i(here d(w _i) represents the distance of a leaf of weight w _i to the root and f is a function defined for nonnegative integers having the property that g(x) = f(x + 1) – f(x) is monotone increasing) over the set of all trees for W having minimal expected length. Minimizing the first two functions was first done by Schwartz [5]. In the case of codes where W is a sequence of probabilities, this implies that the codes based on T have all their absolute central moments minimal. In particular, they are the least variance codes which were also described by Kou [3]. Furthermore, there exists a Huffman tree T, (T-down) which maximizes the functions considered above.However, if g(x) is monotone decreasing, T and T, respectively maximize and minimize f(d(w_i) w _i) over the set of all trees for W having minimal expected length. In addition, we derive a number of interesting results about the distribution of labels within Huffman trees. By suitable modifications of the usual Huffman tree construction, (see [1]) T and T can also be constructed in time O(n log n).     

Nonpreemptive scheduling of periodic tasks in uni- and multiprocessor systems

In this paper we study the problem of scheduling a set of periodic tasks nonpreemptively in hard-real-time systems, where it is critical for all requests of the tasks to be processed in time. A taskT is characterized by itsarrival time A, itsperiod P, and itsexecution time C. Starting fromA, a new request ofT arrives in everyP units of time, requestingC units of processing time, and itsdeadline coincides with the arrival of the next request ofT. All requests must be processed nonpreemptively to meet their corresponding deadlines. We show that the problem of testing the feasibility of a given task set {T ¹,T ²,,T ⁿ} satisfyingP ₁₊₁=k_i p_i, wherek _i; is an integer 1 for 1i n–1, on a single processor is NP-hard in the strong sense, even if all tasks have the same arrival time. For task sets satisfyingP ⁱ⁺¹=K Pⁱ, whereK is an integer 2 for 1 i n–1 and all tasks have the same arrival time, we present linear-time (in the number of requests) optimal scheduling algorithms as well as linear-time (in the number of tasks, i.e.,n) algorithms for testing feasibility in both uniprocessor and multiprocessor systems. We also extend our results to more general task sets.     

A Modular Algorithm for Computing Cohomologies of Lie Algebras and Superalgebras

The paper describes an improved algorithm for computing cohomologies of Lie (super)algebras. The original algorithm developed earlier by the author of this paper is based on the decomposition of the entire cochain complex into minimal subcomplexes. The suggested improvement consists in the replacement of the arithmetic of rational or integer numbers by a more efficient arithmetic of modular fields and the use of the relationship dim H ^k( _p) dimH ^k() between the dimensions of cohomologies over an arbitrary modular field _p = /p and the filed of rational numbers . This inequality allows us to rapidly find subcomplexes for which dimH ^k( _p) > 0 (the number of such subcomplexes is usually not great) using computations over an arbitrary _p and, then, carry out all required computations over in these subcomplexes.     

Methods for optimization of nonlinear problems with discrete variables: A review

The methods for discrete-integer-continuous variable nonlinear optimization are reviewed. They are classified into the following six categories: branch and bound, simulated annealing, sequential linearization, penalty functions, Lagrangian relaxation, and other methods. Basic ideas of each method are described and details of some of the algorithms are given. They are transcribed into a step-by-step format for easy implementation into a computer. Under other methods, rounding-off, heuristic, cutting-plane, pure discrete, and genetic algorithms are described. For nonlinear problems, none of the methods are guaranteed to produce the global minimizer; however, good practical solutions can be obtained.Notation BBM branch and bound method - D set of discrete values for all the discrete variables - D _i set of discrete values for thei-th variable - d _ij j-th discrete value for thei-th variable - f cost function to be minimized - f ^* upper bound for the cost function - g _i i-th constraint function - IP integer programming - ILP integer linear programming - L Lagrangian - LP linear programming - m total number of constraints - MDLP mixed-discrete linear programming - MDNLP mixed-discrete nonlinear programming - n _d number of discrete variables - NLP nonlinear programming - p number of equality constraints; acceptance probability used in simulated annealing - q _i number of discrete values for thei-th variable - SLP sequential linear programming - SQP sequential quadratic programming - x design variable vector of dimension n - x _iL smallest allowed value for thei-th variable - x _iU largest allowed value for thei-th variable - the gradient operator     

Mesh Selection for a Nearly Singular Boundary Value Problem

In this paper, we investigate the numerical solution of a model equation u _xx = exp(– ) (and several slightly more general problems) when 1 using the standard central difference scheme on nonuniform grids. In particular, we are interested in the error behaviour in two limiting cases: (i) the total mesh point number N is fixed when the regularization parameter 0, and (ii) is fixed when N. Using a formal analysis, we show that a generalized version of a special piecewise uniform mesh 12 and an adaptive grid based on the equidistribution principle share some common features. And the optimal meshes give rates of convergence bounded by |log()| as 0 and N is given, which are shown to be sharp by numerical tests.     

On a Stroboscopic Approach to Quantum Tomography of Qudits Governed by Gaussian Semigroups

In this paper, we discuss the minimal number of observables Q ₁, ..., Q , where expectation values at some time instants t ₁, ..., t _r determine the trajectory of a d-level quantum system (qudit) governed by the Gaussian semigroup . We assume that the macroscopic information about the system in question is given by the mean values E _j(Q _i) = tr(Q _i(t _j)) of n selfadjoint operators Q ₁, ..., Q _n at some time instants t ₁ < t ₂ < ... < t _r, where n < d ²– 1 and r deg (, ). Here (, ) stands for the minimal polynomial of the generator of the Gaussian flow (t).     

How to find Steiner minimal trees in euclideand-space

This paper has two purposes. The first is to present a new way to find a Steiner minimum tree (SMT) connectingN sites ind-space,d >- 2. We present (in Appendix 1) a computer code for this purpose. This is the only procedure known to the author for finding Steiner minimal trees ind-space ford > 2, and also the first one which fits naturally into the framework of backtracking and branch-and-bound. Finding SMTs of up toN = 12 general sites ind-space (for anyd) now appears feasible.We tabulate Steiner minimal trees for many point sets, including the vertices of most of the regular and Archimedeand-polytopes with <- 16 vertices. As a consequence of these tables, the Gilbert-Pollak conjecture is shown to be false in dimensions 3–9. (The conjecture remains open in other dimensions; it is probably false in all dimensionsd withd 3, but it is probably true whend = 2.)The second purpose is to present some new theoretical results regarding the asymptotic computational complexity of finding SMTs to precision .We show that in two-dimensions, Steiner minimum trees may be found exactly in exponential time O(C ^N ) on a real RAM. (All previous provable time bounds were superexponential.) If the tree is only wanted to precision , then there is an (N/)^O(N)-time algorithm, which is subexponential if 1/ grows only polynomially withN. Also, therectilinear Steiner minimal tree ofN points in the plane may be found inN ^O(N) time.J. S. Provan devised an O(N ⁶/⁴)-time algorithm for finding the SMT of a convexN-point set in the plane. (Also the rectilinear SMT of such a set may be found in O(N ⁶) time.) One therefore suspects that this problem may be solved exactly in polynomial time. We show that this suspicion is in fact true—if a certain conjecture about the size of Steiner sensitivity diagrams is correct.All of these algorithms are for a real RAM model of computation allowing infinite precision arithmetic. They make no probabilistic or other assumptions about the input; the time bounds are valid in the worst case; and all our algorithms may be implemented with a polynomial amount of space. Only algorithms yielding theexact optimum SMT, or trees with lengths (1 + ) × optimum, where is arbitrarily small, are considered here.     

On Steiner minimal trees withLp distance

LetL _p be the plane with the distanced _p (A ₁ ,A ₂ ) = (¦x ₁ –x ₂¦ ^p + ¦y₁ –y ₂¦^p)^/1p wherex _i andy _i are the cartesian coordinates of the pointA _i . LetP be a finite set of points inL _p . We consider Steiner minimal trees onP. It is proved that, for 1 <p < , each Steiner point is of degree exactly three. Define the Steiner ratio _p to be inf{L _s (P)/L _m (P)¦PL _p } whereL _s (P) andL _m (P) are lengths of the Steiner minimal tree and the minimal spanning tree onP, respectively. Hwang showed ₁ = 2/3. Chung and Graham proved ₂ > 0.842. We prove in this paper that {} = 2/3 and (2/2)_{12 p} 3/2 for anyp.This work was supported in part by the National Science Foundation of China and the President Foundation of Academia Sinica.     

PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples

We describe a polynomial-time algorithm for learning axis-aligned rectangles in Q ^d with respect to product distributions from multiple-instance examples in the PAC model. Here, each example consists of n elements of Q^d together with a label indicating whether any of the n points is in the rectangle to be learned. We assume that there is an unknown product distribution D over Q ^d such that all instances are independently drawn according to D. The accuracy of a hypothesis is measured by the probability that it would incorrectly predict whether one of n more points drawn from D was in the rectangle to be learned. Our algorithm achieves accuracy with probability 1- in O (d⁵ n¹²/²⁰ log² nd/ time.     

On a random Volterra integral equation

Summary Tsokos [12] showed the existence of a unique random solution of the random Volterra integral equation (*)x(t; ) = h(t; ) + _o ^t k(t, ; )f(, x(; )) d, where , the supporting set of a probability measure space (,A, P). It was required thatf must satisfy a Lipschitz condition in a certain subset of a Banach space. By using an extension of Banach's contraction-mapping principle, it is shown here that a unique random solution of (*) exists whenf is (, )-uniformly locally Lipschitz in the same subset of the Banach space considered in [12].