sequential sequence estimation for channels with intersymbol interference of finite or infinite length a sequential sequence estimator for channels with finite or infinite intersymbol interference isi is proposed it consists of a whitened matched filter followed by a sequential algorithm a key component of a sequential estimator is a suitable metric that accounts for the unequal search depth this metric is derived for the isi channel computer simulations for three different channels show that the sequential sequence estimator has considerably less computational complexity than a maximum likelihood sequence estimator using the viterbi algorithm mlse va while still maintaining the optimum estimation performance of the mlse va a performance not achievable with any reduced state viterbi algorithm

delayed decision feedback sequence estimation the authors delayed decision feedback sequence estimation ddfse for uncoded pam signals is considered estimates on the performance of the algorithm are given and simulation results are provided for several examples a more general form of ddfse applicable to coded modulation systems is also presented as an example detection of trellis coded qpsk quadrature phase shift keyed signals over intersymbol interference channels is discussed

a minmax regret approach to the critical path method with task interval times abstract the execution of a given project with a number of interrelated tasks due to precedence constraints represents a challenge when one must to control the available resources and the compromised due dates in this paper we analyse this problem under uncertain individual task completing times specifically we will assume that a given range for the admissible values of each individual completing time is available taking into account that the precedence relations between tasks must be preserved each realization of the admissible execution times for the set of tasks will define a new scenario determining the ending time for the project and the subset of critical tasks the minmax regret criterion will be used in order to obtain a robust approximation to the critical set of tasks determining the overall execution time for the project

ordered weighted average optimization in multiobjective spanning tree problem abstract multiobjective spanning tree problems are studied in this paper the ordered median objective function is used as an averaging operator to aggregate the vector of objective values of feasible solutions this leads to the ordered weighted average spanning tree problem a nonlinear combinatorial optimization problem different mixed integer linear programs are proposed based on the most relevant minimum cost spanning tree models in the literature these formulations are analyzed and several enhancements presented their empirical performance is tested over a set of randomly generated benchmark instances the results of the computational experiments show that the choice of an appropriate formulation allows to solve larger instances with more objectives than those previously solved in the literature

gmv control of non linear continuous time systems including common delays and state space models a non linear generalized minimum variance control law is proposed for the control of non linear continuous time multivariable systems with common delays on input and output channels the quadratic cost index involves both error and control signal costing terms the solution for the control law is obtained using a non linear operator representation of the plant and a linear state equation model for the disturbance and reference models the reference and disturbance models are represented by linear subsystems however the plant model can be in a very general non linear operator form which could involve state space transfer operators or non linear function look up tables the structure of the system and criterion is chosen so that a simple controller structure and solution is obtained the controller obtained is simple to implement particularly in one form which might be considered to be a state space version of a non linear smith predictor the results are related to those for discrete time systems but

reduced complexity sequence estimation using state partitioning a reduced state sequence estimator rsse for linear dispersive channels is described the technique is based on partitioning the set of all channel states in a way that defines a trellis with fewer states the new technique generalizes trellises that were previously obtained by duel hallen and heegard 1989 and eyouboglu and qureshi 1988 while the approach followed by duel hallen results in powers of two state trellises the new technique generates trellises with an arbitrary number of states using the state partitioning approach one could also generalize the trellises obtained by eyouboglu therefore the approach described in this paper provides a better performance complexity tradeoff

minimax regret spanning arborescences under uncertain costs abstract the paper considers a classical optimization problem on a network whose arc costs are partially known it is assumed that an interval estimate is given for each arc cost and no further information about the statistical distribution of the truth value of the arc cost is known in this context given a spanning arborescence in the network its cost can take on different values according to the choice of each individual arc cost that is according to the different cost scenarios we analyze the problem of finding which spanning arborescence better approaches the optimal one under each possible scenario the minimax regret criterion is proposed in order to obtain such a robust solution to the problem in the paper it is shown that a greedy type algorithm can compute an optimal solution of this problem on acyclic networks for general networks the problem becomes np hard in this case the special structure of the optimization problem allows us to design a bounding process for the optimum value that will result in a heuristic algorithm described at the end of the paper

ngmv control of delayed piecewise affine systems a nonlinear generalized minimum variance ngmv control algorithm is introduced for the control of piecewise affine pwa systems under some conditions discrete time pwa systems can be transferred into an equivalent state dependent nonlinear system form the equivalent state dependent systems maintain the hybrid nature of the original pwa systems and include both the discrete and continuous signals in one general description in a more general way the process is assumed to include common delays in input or output channels of magnitude then the ngmv control strategy can be applied the ngmv controller is related to a well known and accepted solution for time delay systems smith predictor but has the advantage that it may stabilize open loop unstable processes

the computational complexity of the relative robust shortest path problem with interval data abstract the paper deals with the relative robust shortest path problem in a directed arc weighted graph where arc lengths are specified as intervals containing possible realizations of arc lengths the complexity status of this problem has been unknown in the literature we show that the problem is np hard

a note on a compatriot of the real marcinkiewicz space the authors discuss a class of functions related to the real marcinkiewicz space which arises in certain optimal control problems and highlight some simple common misunderstandings about the structure of this class

a probabilistic model for minmax regret in combinatorial optimization in this paper we propose a new probabilistic model for minimizing the anticipated regret in combinatorial optimization problems with distributional uncertainty in the objective coefficients the interval uncertainty representation of data is supplemented with information on the marginal distributions as a decision criterion we minimize the worst case conditional value at risk of regret the proposed model includes the interval data minmax regret model as a special case for the class of combinatorial optimization problems with a compact convex hull representation a polynomial sized mixed integer linear program is formulated when a the range and mean are known and b the range mean and mean absolute deviation are known and a mixed integer second order cone program is formulated when c the range mean and standard deviation are known for the subset selection problem of choosing k elements of maximum total weight out of a set of n elements the probabilistic regret model is shown to be solvable in polynomial time in the instances a and b above this extends the current known polynomial complexity result for minmax regret subset selection with range information only

multistep detector for linear isi channels incorporating degrees of belief in past estimates this paper formulates the channel equalization problem in the framework of constrained maximum likelihood estimation this allows us to highlight key issues including the need to summarize past data and to apply a finite alphabet constraint over a sliding optimization window the approach adopted here leads to embellishments of the usual nonadaptive decision feedback equalizer and its multistep extensions it includes a provision for degrees of belief in past estimates which addresses the problem of error propagation

deterministic risk control for cost effective network connections this paper considers the minimum connection problem in networks with uncertain data in such a network it is assumed that one can establish a link e by paying a cost c e in a given interval c e c e while taking a risk c e c e c e c e of link failure we develop polynomial time algorithms for minimum cost network connection with paths or spanning trees under risk sum constraints

on accuracy robustness and tolerances in vector boolean optimization a boolean programming problem with a finite number of alternatives where initial coefficients costs of linear payoff functions are subject to perturbations is considered we define robust solution as a feasible solution which for a given set of realizations of uncertain parameters guarantees the minimum value of the worst case relative regret among all feasible solutions for the pareto optimality principle an appropriate definition of the worst case relative regret is specified it is shown that this definition is closely related to the concept of accuracy function being recently intensively studied in the literature we also present the concept of robustness tolerances of a single cost vector the tolerance is defined as the maximum level of perturbation of the cost vector which does not destroy the solution robustness we present formulae allowing the calculation of the robustness tolerance obtained for some initial costs the results are illustrated with several numerical examples

reduced complexity soft mimo detection based on causal and noncausal decision feedback we present a reduced complexity soft detection rcsd scheme geared to multiple input multiple output mimo systems with spatial domain multiplexing leading to layered space time or space frequency architecture the proposed algorithm relies on a trellis representation of the mimo signals and a subsequent formulation of constrained depth maximum a posterior map detection in conjunction with soft decision feedback sdf decision feedback is broken into causal and noncausal parts in an effort to maximize the observation window while maintaining a reasonable computational load two variations of rcsd are proposed to utilize the noncausal information in different ways analysis based on mean squared error mse and log likelihood ratio llr associated with symbols in different stages of the trellis is done to develop insight into error propagation in the rcsd algorithm as well as to compare the quality of the soft information obtained by the two rcsd variants error rate simulations are conducted in the context of turbo like iterative demapping and decoding idd the resulting performance and required complexity are compared with those of maximum a posterior map detection soft sphere detection sd as well as the turbo blast processing scheme we observe favorable performance complexity tradeoffs with the proposed soft detection scheme for a number of modulation channel scenarios

restricted robust uniform matroid maximization under interval uncertainty for the problem of selecting p items with interval objective function coefficients so as to maximize total profit we introduce the r restricted robust deviation criterion and seek solutions that minimize the r restricted robust deviation this new criterion increases the modeling power of the robust deviation minmax regret criterion by reducing the level of conservatism of the robust solution it is shown that r restricted robust deviation solutions can be computed efficiently results of experiments and comparisons with absolute robustness robust deviation and restricted absolute robustness criteria are reported

a benders decomposition approach for the robust spanning tree problem with interval data abstract the robust spanning tree problem is a variation motivated by telecommunications applications of the classic minimum spanning tree problem in the robust spanning tree problem edge costs lie in an interval instead of having a fixed value interval numbers model uncertainty about the exact cost values a robust spanning tree is a spanning tree whose total cost minimizes the maximum deviation from the optimal spanning tree over all realizations of the edge costs this robustness concept is formalized in mathematical terms and is used to drive optimization this paper describes a new exact method based on benders decomposition for the robust spanning tree problem with interval data computational results highlight the efficiency of the new method which is shown to be very fast on all the benchmarks considered and in particular on those that were harder to solve for the methods previously known

using bounds on the data in linear programming the tolerance approach to sensitivity analysis in contrast to traditional sensitivity analysis in linear programming the tolerance approach considers simultaneous and independent variations in a number of parameters a primary focus of this approach is to determine a maximum tolerance percentage for selected right hand side terms in which the same basis is optimal as long as each term is accurate to within that percentage of its estimated value similarly the approach yields a maximum tolerance percentage for selected objective function coefficients this paper shows how the tolerance approach can exploit information on the range of possible values over which terms and coefficients can vary to yield larger maximum tolerance percentages

novel reduced state bcjr algorithms bcjr algorithm is an exact and efficient algorithm to compute the marginal posterior distributions of state variables and pairs of consecutive state variables of a trellis structure due to its overwhelming complexity reduced complexity variations such as the m bcjr algorithm have been developed in this paper we propose improvements upon the conventional m bcjr algorithm based on modified active state selection criteria we propose selecting the active states based on estimates of the fixed lag smoothed distributions of the state variables we also present gaussian approximation techniques for the low complexity estimation of these fixed lag smoothed distributions the improved performance over the m bcjr algorithm is shown via computer simulations

on a constant factor approximation for minmax regret problems using a symmetry point scenario abstract in order to find a robust solution under an unknown linear cost function it will be considered the minmax regret criterion it is assumed the vector of costs can take on values from a given uncertainty set the resulting optimization model has been extensively analyzed in the literature when the uncertain costs are modeled by closed intervals unfortunately except for rare applications this problem has np hard complexity which has led to the appearance of approximated methods seeking for good solutions in a short computational time the most recent approaches solve the deterministic version of the optimization problem under the midpoint scenario that realization in which each cost takes the midpoint of the closed interval proceeding in this way it can be obtained a 2 approximation for a wide class of minmax regret optimization models in this paper it is shown how this approach can be extended to a new class of minmax regret problems including more general uncertainty sets

reduced state sequence estimation with set partitioning and decision feedback a reduced state sequence estimator for linear intersymbol interference channels is described the estimator uses a conventional viterbi algorithm with decision feedback to search a reduced state subset trellis that is constructed using set partitioning principles the complexity of maximum likelihood sequence estimation mlse due to the length of the channel memory and the size of the signal set is systematically reduced an error probability analysis shows that a good performance complexity tradeoff can be obtained in particular the results indicate that the required complexity to achieve the performance of mlse is independent of the size of the signal set for large enough signal sets simulation results are provided for two partial response systems a simple technique for quadrature partial response signaling qprs is described that eliminates the quasicatastrophic nature of the ml trellis

on the complexity of a class of combinatorial optimization problems with uncertainty we consider a robust minmax regret version of the problem of selecting p elements of minimum total weight out of a set of m elements with uncertainty in weights of the elements we present a polynomial algorithm with the order of complexity o min p m p 2m for the case where uncertainty is represented by means of interval estimates for the weights we show that the problem is np hard in the case of an arbitrary finite set of possible scenarios even if there are only two possible scenarios this is the first known example of a robust combinatorial optimization problem that is np hard in the case of scenario represented uncertainty but is polynomially solvable in the case of the interval representation of uncertainty