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Waveform design is a pivotal component of the fully adaptive radar construct. In this paper, we consider waveform design for radar space time adaptive processing (STAP), accounting for the waveform dependence of the clutter correlation matrix. Due to this dependence, in general, the joint problem of receiver filter optimization and radar waveform design becomes an intractable, nonconvex optimization problem, Nevertheless, it is, however, shown to be individually convex either in the filter or in the waveform variables. We derive constrained versions of a) the alternating minimization algorithm, b) proximal alternating minimization, and c) the constant modulus alternating minimization, which, at each step, iteratively optimizes either the STAP filter or the waveform independently. A fast and slow time model permits waveform design in radar STAP, but the primary bottleneck is the computational complexity of the algorithms. [ABSTRACT FROM PUBLISHER]

This paper introduces a generalization of the frequently discussed problem of finding the optimum redundancy that maximizes the reliability of a network of components. Past work has restricted consideration to arrangements of redundant components called series-parallel networks. This paper allows a much broader class of arrangements called series-parallelseries networks. It is important to consider such arrangements for realistic situations in which components have more than one failure mode, or the combination of parallel paths introduces a failure probability. A dynamic programming algorithm is used to solve the more general problem for the case in which there are no constraints on the optimum solution. The algorithm is extended to handle multiple constraints using dominance and a variety of elimination methods to reduce the storage required in a compurer implementation of the algorithm. Problems with as many as 15 serial components and three constraints have been solved with reasonable digital computer computation times. [ABSTRACT FROM AUTHOR]

This paper presents an efficient technique for synthesis and optimization of the polynomials over GF(2m), where m is a nonzero positive integer. The technique is based on a graph-based decomposition and factorization of the polynomials, followed by efficient network factorization and optimization. A technique for efficiently computing the coefficients of the polynomials over GF(pm), where p is a prime number, is first presented. The coefficients are stored as polynomial graphs over GF(pm). The synthesis and optimization is initiated from this graph-based representation. The technique has been applied to minimize multipliers over the fields GF(2κ ), where k = 2,..., 8, generated with all the 51 primitive polynomials in the 0.18-/~m CMOS technology with the help of the Synopsys design compiler. It has also been applied to minimize combinational exponentiation circuits, parallel integer adders and multipliers, and other multivariate bit- as well as word-level polynomials. The experimental results suggest that the proposed technique can reduce area, delay, and power by significant amounts. We also observed that the technique is capable of producing 100% testable circuits for stuck-at faults. [ABSTRACT FROM AUTHOR]

In this paper, the concept of second order generalized α-type I univexity is introduced. Based on the new definitions, we derive weak, strong and strict converse duality results for two second order duals of a minmax fractional programming problem. [ABSTRACT FROM AUTHOR]

In this paper, we study an algorithm (which we call the support reduction algorithm) that can be used to compute non-parametric M-estimators in mixture models. The algorithm is compared with natural competitors in the context of convex regression and the ‘Aspect problem’ in quantum physics. [ABSTRACT FROM AUTHOR]

A key challenge in using timing-robust asynchronous circuit styles is the lack of automated optimization techniques. In this paper, technology mapping and cell merger algorithms for asynchronous threshold networks are introduced. The cell merger problem is a restricted form of technology mapping where only adjacent cells are merged. The two algorithms can each target either delay or area, or a combined delay-area cost function. Experiments were performed on substantial industrial design examples (DES and GCD circuits) that had already been optimized by an existing commercial asynchronous synthesis tool flow. Average delay improvements of 31.6% for basic technology mapping and 29.6% for basic cell merger were obtained. Average area improvements of 9.5 % for basic technology mapping and 8.5 % for basic cell merger were also obtained. Additional experiments were performed on the largest MCNC combinational benchmarks with similar results. Finally, targeting a hybrid cost function, area minimization under specified hard timing constraints, a further area reduction of up to 10.7% on average in technology mapping was recovered without compromising the overall system performance. The new algorithms are the first systematic and general mapping approach for asynchronous threshold networks, targeting delay or area, which preserve the timing-robustness properties of the initial unoptimized circuits. [ABSTRACT FROM AUTHOR]

In this paper we discuss local uniqueness, continuity, and differentiability properties of solutions of parameterized variational inequalities (generalized equations). To this end we use two types of techniques. One approach consists in formulating variational inequalities in a form of optimization problem based on regularized gap functions, and applying a general theory of perturbation analysis of parameterized optimization problems. Another approach is based on a theory of contingent (outer graphical) derivatives and some results about differentiability properties of metric projections. [ABSTRACT FROM AUTHOR]

In the last decades, heuristic algorithms are widely used in solving problems in different fields of science and engineering. Most of these methods are inspired by natural phenomena, such as biological behaviours or physical principles. In this paper, a new optimization method based on the dynamic of sliding motion along a frictionless inclined plane is introduced. In the proposed algorithm, a collection of agents cooperate with each other and move toward better positions in the search space by employing Newton's second law and equations of motion. Our method is compared with other popular optimization algorithms and the results on 23 standard benchmark functions show its effectiveness in most cases. [ABSTRACT FROM AUTHOR]

We study analyticity, differentiability, and semismoothness of Löwner's operator and spectral functions under the framework of Euclidean Jordan algebras. In particular, we show that many optimization-related classical results in the symmetric matrix space can be generalized within this framework. For example, the metric projection operator over any symmetric cone defined in a Euclidean Jordan algebra is shown to be strongly semismooth. The research also raises several open questions, whose answers would be of strong interest for optimization research. [ABSTRACT FROM AUTHOR]

It is shown that the synthesis of robust H∞-control with respect to the output for systems with unknown limited parameters reduces to the solution of an optimization problem under constraints prescribed by a system of linear matrix inequalities. For this kind of problems, an optimization algorithm implemented with the use of standard procedures of the MATLAB package is suggested.The algorithm effectiveness is illustrated by the example of an optimal dampening of vibrations of a parametrically perturbed pendulum. [ABSTRACT FROM AUTHOR]

A major difficulty in practicing constrained nonlinear optimization is that different optimization algorithms are effective for different classes of problems. The search algorithms tested were selected because they seemingly had the capability of solving ill-defined problems. The emphasis of this research was on problems that are realistic and do not necessarily satisfy the classical assumptions of convexity, continuity, unimodality, etc. All algorithms were tested on a variety of problems. A problem was described by three types of attributes: the environmental attributes, the objective function attributes, and the constraint set attributes. The results of the study provide selective logic for choosing an effective search algorithm. [ABSTRACT FROM AUTHOR]