There have been a number of different versioning models proposed. The research in this area can be divided into two categories: object versioning and schema versioning. In this dissertation, both problem domains are considered as a single unit. This dissertation describes a unifying version model (UVM) for maintaining changes to both objects and schema. UVM handles schema versioning operations by using object versioning techniques. The result is that the UVM allows the OODBMS to be much smaller than previous systems. Also, programmers need know only one set of versioning operations; thus, reducing the learning time by half. This dissertation shows that UVM is a simple but semantically sound and powerful version model for both objects and schema.
Discrete event simulation is an important tool for modeling and analysis. Some of the simulation applications such as telecommunication network performance, VLSI logic circuits design, battlefield simulation, require enormous amount of computing resources. One way to satisfy this demand for computing power is to decompose the simulation system into several logical processes (Ip) and run them concurrently. In any parallel discrete event simulation (PDES) system, the events are ordered according to their time of occurrence. In order for the simulation to be correct, this ordering has to be preserved. There are three approaches to maintain this ordering. In a conservative system, no lp executes an event unless it is certain that all events with earlier time-stamps have been executed. Such systems are prone to deadlock. In an optimistic system on the other hand, simulation progresses disregarding this ordering and saves the system states regularly. Whenever a causality violation is detected, the system rolls back to a state saved earlier and restarts processing after correcting the error. There is another approach in which all the lps participate in the computation of a safe time-window and all events with time-stamps within this window are processed concurrently. In optimistic simulation systems, there is a global virtual time (GVT), which is the minimum of the time-stamps of all the events existing in the system. The system can not rollback to a state prior to GVT and hence all such states can be discarded. GVT is used for memory management, load balancing, termination detection and committing of events. However, GVT computation introduces additional overhead. In optimistic systems, large number of rollbacks can degrade the system performance considerably. We have studied the effect of load balancing in reducing the number of rollbacks in such systems. We have designed three load balancing algorithms and implemented two of …
The Programmable Logic Array (PLA) has been widely used in the design of VLSI circuits and systems because of its regularity, flexibility, and simplicity. The equivalence problem is typically to verify that the final description of a circuit is functionally equivalent to its initial description. Verifying the functional equivalence of two descriptions is equivalent to proving their logical equivalence. This problem of pure logic is essential to circuit design. The most widely used technique to solve the problem is based on Binary Decision Diagram or BDD, proposed by Bryant in 1986. Unfortunately, BDD requires too much time and space to represent moderately large circuits for equivalence testing. We design and implement a new algorithm called the Cover-Merge Algorithm for the equivalence problem based on a divide-and-conquer strategy using the concept of cover and a derivational method. We prove that the algorithm is sound and complete. Because of the NP-completeness of the problem, we emphasize simplifications to reduce the search space or to avoid redundant computations. Simplification techniques are incorporated into the algorithm as an essential part to speed up the the derivation process. Two different sets of heuristics are developed for two opposite goals: one for the proof of equivalence and the other for its disproof. Experiments on a large scale of data have shown that big speed-ups can be achieved by prioritizing the heuristics and by choosing the most favorable one at each iteration of the Algorithm. Results are compared with those for BDD on standard benchmark problems as well as on random PLAs to perform an unbiased way of testing algorithms. It has been shown that the Cover-Merge Algorithm outperforms BDD in nearly all problem instances in terms of time and space. The algorithm has demonstrated fairly stabilized and practical performances especially for big PLAs under a wide …
Surface fitting methods play an important role in many scientific fields as well as in computer aided geometric design. The problem treated here is that of constructing a smooth surface that interpolates data values associated with scattered nodes in the plane. The data is said to be convex if there exists a convex interpolant. The problem of convexity-preserving interpolation is to determine if the data is convex, and construct a convex interpolant if it exists.
DNA sequence analysis involves precise discrimination of two of the sequence's most important components: exons and introns. Exons encode the proteins that are responsible for almost all the functions in a living organism. Introns interrupt the sequence coding for a protein and must be removed from primary RNA transcripts before translation to protein can occur. A pattern recognition technique called Finite Induction (FI) is utilized to study the language of exons and introns. FI is especially suited for analyzing and classifying large amounts of data representing sequences of interest. It requires no biological information and employs no statistical functions. Finite Induction is applied to the exon and intron components of DNA by building a collection of rules based upon what it finds in the sequences it examines. It then attempts to match the known rule patterns with new rules formed as a result of analyzing a new sequence. A high number of matches predict a probable close relationship between the two sequences; a low number of matches signifies a large amount of difference between the two. This research demonstrates FI to be a viable tool for measurement when known patterns are available for the formation of rule sets.
This research focused on the off-line cursive script recognition application. The problem is very large and difficult and there is much room for improvement in every aspect of the problem. Many different aspects of this problem were explored in pursuit of solutions to create a more practical and usable off-line cursive script recognizer than is currently available.
The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.
The main contribution of this dissertation proposes a new paradigm, called the parentheses matching paradigm. It claims that this paradigm is well suited for designing efficient parallel algorithms for a broad class of nonnumeric problems. To demonstrate its applicability, we present three cost-optimal parallel algorithms for breadth-first traversal of general trees, sorting a special class of integers, and coloring an interval graph with the minimum number of colors.
This study examines the performance of concurrent algorithms for B-trees and linear hashing. B-trees are widely used as an access method for large, single key, database files, stored in lexicographic order on secondary storage devices. Linear hashing is a fast and reliable hash algorithm, suitable for accessing records stored unordered in buckets. This dissertation presents performance results on implementations of concurrent Bunk-tree and linear hashing algorithms, using lock-based, partitioned and distributed methods on the Sequent Symmetry shared memory multiprocessor system and on a network of distributed processors created with PVM (Parallel Virtual Machine) software. Initial experiments, which started with empty data structures, show good results for the partitioned implementations and lock-based linear hashing, but poor ones for lock-based Blink-trees. A subsequent test, which started with loaded data structures, shows similar results, but with much improved performances for locked Blink- trees. The data also highlighted the high cost of split operations, which reached up to 70% of the total insert time.
This thesis presents a simple method for the concurrent manipulation of linearly ordered data structures on hypercubes. The method is based on the existence of a pruned binomial search tree rooted at any arbitrary node of the binary hypercube. The tree spans any arbitrary sequence of n consecutive nodes containing the root, using a fan-out of at most [log₂ 𝑛] and a depth of [log₂ 𝑛] +1. Search trees spanning non-overlapping processor lists are formed using only local information, and can be used concurrently without contention problems. Thus, they can be used for performing broadcast and merge operations simultaneously on sets with non-uniform sizes. Extensions to generalized and faulty hypercubes and applications to image processing algorithms and for m-way search are discussed.
New peripheral devices are being developed at an ever increasing rate. Before such accessories can be used in the UNIX environment (UNIX is a trademark of Bell Laboratories), they must be able to communicate with the operating system. This involves writing a device driver for each device. In order to do this, very detailed knowledge is required of both the device to be integrated and the version of UNIX to which it will be attached. The process is long, detailed and prone to subtle problems and errors. This paper presents a menu-driven utility designed to simplify and accelerate the design and implementation of UNIX device drivers by freeing developers from many of the implementation specific low-level details.
In this thesis, the gate matrix layout problem in VLSI design is considered where the goal is to minimize the number of tracks required to layout a given circuit and a taxonomy of approaches to its solution is presented. An efficient hybrid heuristic is also proposed for this combinatorial optimization problem, which is based on the combination of probabilistic hill-climbing technique and greedy method. This heuristic is tested experimentally with respect to four existing algorithms. As test cases, five benchmark problems from the literature as well as randomly generated problem instances are considered. The experimental results show that the proposed hybrid algorithm, on the average, performs better than other heuristics in terms of the required computation time and/or the quality of solution. Due to the computation-intensive nature of the problem, an exact solution within reasonable time limits is impossible. So, it is difficult to judge the effectiveness of any heuristic in terms of the quality of solution (number of tracks required). A probabilistic model of the gate matrix layout problem that computes the expected number of tracks from the given input parameters, is useful to this respect. Such a probabilistic model is proposed in this thesis, and its performance is experimentally evaluated.
Genetic algorithm and artificial life techniques are applied to the development of challenging and interesting opponents in a combat-based computer game. Computer simulations are carried out against an idealized human player to gather data on the effectiveness of the computer generated opponents.
There are three main results in this dissertation. They are PLS-completeness of discrete Hopfield network convergence with eight different restrictions, (degree 3, bipartite and degree 3, 8-neighbor mesh, dual of the knight's graph, hypercube, butterfly, cube-connected cycles and shuffle-exchange), exponential convergence behavior of discrete Hopfield network, and simulation of Turing machines by discrete Hopfield Network.
The goal of a parallel algorithm is to solve a single problem using multiple processors working together and to do so in an efficient manner. In this regard, there is a need to categorize strategies in order to solve broad classes of problems with similar structures and requirements. In this dissertation, two parallel algorithm design strategies are considered: linked list ranking and parentheses matching.
In this thesis, a framework for Quality of Service provisioning in next generation wireless access networks is proposed. The framework aims at providing a differentiated service treatment to real-time (delay-sensitive) and non-real-time (delay-tolerant) multimedia traffic flows at the link layer. Novel techniques such as bandwidth compaction, channel reservation, and channel degradation are proposed. Using these techniques, we develop a call admission control algorithm and a call control block as part of the QoS framework. The performance of the framework is captured through analytical modeling and simulation experiments. By analytical modeling, the average carried traffic and the worst case buffer requirements for real-time and non-real-time calls are estimated. Simulation results show a 21% improvement in call admission probability of real-time calls, and a 17% improvement for non-real-time calls, when bandwidth compaction is employed. The channel reservation technique shows a 12% improvement in call admission probability in comparison with another proposed scheme in the literature.
Learning and representing and reasoning about temporal relations, particularly causal relations, is a deep problem in artificial intelligence (AI). Learning such representations in the real world is complicated by the fact that phenomena are subject to multiple time scale influences and may operate with a strange attractor dynamic. This dissertation proposes a new computational learning mechanism, the adaptrode, which, used in a neuromimic processing architecture may help to solve some of these problems. The adaptrode is shown to emulate the dynamics of real biological synapses and represents a significant departure from the classical weighted input scheme of conventional artificial neural networks. Indeed the adaptrode is shown, by analysis of the deep structure of real synapses, to have a strong structural correspondence with the latter in terms of multi-time scale biophysical processes. Simulations of an adaptrode-based neuron and a small network of neurons are shown to have the same learning capabilities as invertebrate animals in classical conditioning. Classical conditioning is considered a fundamental learning task in animals. Furthermore, it is subject to temporal ordering constraints that fulfill the criteria of causal relations in natural systems. It may offer clues to the learning of causal relations and mechanisms for causal reasoning. The adaptrode is shown to solve an advanced problem in classical conditioning that addresses the problem of real world dynamics. A network is able to learn multiple, contrary associations that separate in time domains, that is a long-term memory can co-exist with a short-term contrary memory without destroying the former. This solves the problem of how to deal with meaningful transients while maintaining long-term memories. Possible applications of adaptrode-based neural networks are explored and suggestions for future research are made.
Numerical methods are usually necessary in solving Hamiltonian systems since there is often no closed-form solution. By utilizing a general property of Hamiltonians, namely the symplectic property, all of the qualities of the system may be preserved for indefinitely long integration times because all of the integral (Poincare) invariants are conserved. This allows for more reliable results and frequently leads to significantly shorter execution times as compared to conventional methods. The resonant triad Hamiltonian with one degree of freedom will be focused upon for most of the numerical tests because of its difficult nature and, moreover, analytical results exist whereby useful comparisons can be made.
The study of multicomputer interconnection networks is an important area of research in parallel processing. We introduce vertex-symmetric Hamming-group graphs as a model to design a wide variety of network topologies including the hypercube network.
In this dissertation, we investigate how commonsense reasoning can be formalized by using extended logic programs. In this investigation, we first use extended logic programs to formalize inheritance hierarchies with exceptions by adopting McCarthy's simple abnormality formalism to express uncertain knowledge. In our representation, not only credulous reasoning can be performed but also the ambiguity-blocking inheritance and the ambiguity-propagating inheritance in skeptical reasoning are simulated. In response to the anomalous extension problem, we explore and discover that the intuition underlying commonsense reasoning is a kind of forward reasoning. The unidirectional nature of this reasoning is applied by many reformulations of the Yale shooting problem to exclude the undesired conclusion. We then identify defeasible conclusions in our representation based on the syntax of extended logic programs. A similar idea is also applied to other formalizations of commonsense reasoning to achieve such a purpose.
The focus of this dissertation is a methodology that enables computer systems to classify different up-front images of human faces as belonging to one of the individuals to which the system has been exposed previously. The images can present variance in size, location of the face, orientation, facial expressions, and overall illumination. The approach to the problem taken in this dissertation can be classified as analytic as the shapes of individual features of human faces are examined separately, as opposed to holistic approaches to face recognition. The outline of the features is used to construct signature functions. These functions are then magnitude-, period-, and phase-normalized to form a translation-, size-, and rotation-invariant representation of the features. Vectors of a limited number of the Fourier decomposition coefficients of these functions are taken to form the feature vectors representing the features in the corresponding vector space. With this approach no computation is necessary to enforce the translational, size, and rotational invariance at the stage of recognition thus reducing the problem of recognition to the k-dimensional clustering problem. A recognizer is specified that can reliably classify the vectors of the feature space into object classes. The recognizer made use of the following principle: a trial vector is classified into a class with the greatest number of closest vectors (in the sense of the Euclidean distance) among all vectors representing the same feature in the database of known individuals. A system based on this methodology is implemented and tried on a set of 50 pictures of 10 individuals (5 pictures per individual). The recognition rate is comparable to that of most recent results in the area of face recognition. The methodology presented in this dissertation is also applicable to any problem of pattern recognition where patterns can be represented as a collection of black …
This investigation proposes a powerful formalization of common sense knowledge based on function-free normal deduction graphs (NDGs) which form a powerful tool for deriving Horn and non-Horn clauses without functions. Such formalization allows common sense reasoning since it has the ability to handle not only negative but also incomplete information.
This dissertation addresses the general problem of recognition of acoustic signals which may be derived from speech, sonar, or acoustic phenomena. The specific problem of recognizing speech is the main focus of this research. The intention is to design a recognition system for a definite number of discrete words. For this purpose specifically, eight isolated words from the T1MIT database are selected. Four medium length words "greasy," "dark," "wash," and "water" are used. In addition, four short words are considered "she," "had," "in," and "all." The recognition system addresses the following issues: filtering or preprocessing, training, and decision-making. The preprocessing phase uses linear predictive coding of order 12. Following the filtering process, a vector quantization method is used to further reduce the input data and generate a finite inductive sequence of symbols representative of each input signal. The sequences generated by the vector quantization process of the same word are factored, and a single ruling or reference template is generated and stored in a codebook. This system introduces a new modeling technique which relies heavily on the basic concept that all finite sequences are finitely inductive. This technique is used in the training stage. In order to accommodate the variabilities in speech, the training is performed casualty, and a large number of training speakers is used from eight different dialect regions. Hence, a speaker independent recognition system is realized. The matching process compares the incoming speech with each of the templates stored, and a closeness ration is computed. A ratio table is generated anH the matching word that corresponds to the smallest ratio (i.e. indicating that the ruling has removed most of the symbols) is selected. Promising results were obtained for isolated words, and the recognition rates ranged between 50% and 100%.
This research establishes the feasibility and potential utility of a software mechanism which employs artificial intelligence techniques to enhance the capabilities of standard discrete event simulators. As background, current methods of integrating artificial intelligence with simulation and relevant research are briefly reviewed.
Because of the high cost and impracticality of a high connectivity network, most recent research in transaction processing has focused on a distributed replicated database system. In such a system, multiple copies of a data item are created and stored at several sites in the network, so that the system is able to tolerate more crash and communication failures and attain higher data availability. However, the multiple copies also introduce a global inconsistency problem, especially in a partitioned network. In this dissertation a tree quorum algorithm is proposed to solve this problem, imposing a logical tree structure along with dynamic system reconfiguration on all the copies of each data item. The proposed algorithm can be viewed as a dynamic voting technique which, with the help of an appropriate concurrency control algorithm, exhibits the major advantages of quorum-based replica control algorithms and of the available copies algorithm, so that a single copy is read for a read operation and a quorum of copies is written for a write operation. In addition, read and write quorums are computed dynamically and independently. As a result expensive read operations, like those that require several copies of a data item to be read in most quorum schemes, are eliminated. Furthermore, the message costs of read and write operations are reduced by the use of smaller quorum sizes. Quorum sizes can be reduced to a constant in a lightly loaded system, and log n in a failure-free network, as well as [n +1/2] in a partitioned network in a heavily loaded system. On average, our algorithm requires fewer messages than the best known tree quorum algorithm, while still maintaining the same upper bound on quorum size. One-copy serializability is guaranteed with higher data availability and highest degree of fault tolerance (up to n - 1 site …
The fusion of computers and communications has promised to herald the age of information super-highway over high speed communication networks where the ultimate goal is to enable a multitude of users at any place, access information from anywhere and at any time. This, in a nutshell, is the goal envisioned by the Personal Communication Services (PCS) and Xerox's ubiquitous computing. In view of the remarkable growth of the mobile communication users in the last few years, the radio frequency spectrum allocated by the FCC (Federal Communications Commission) to this service is still very limited and the usable bandwidth is by far much less than the expected demand, particularly in view of the emergence of the next generation wireless multimedia applications like video-on-demand, WWW browsing, traveler information systems etc. Proper management of available spectrum is necessary not only to accommodate these high bandwidth applications, but also to alleviate problems due to sudden explosion of traffic in so called hot cells. In this dissertation, we have developed simple load balancing techniques to cope with the problem of tele-traffic overloads in one or more hot cells in the system. The objective is to ease out the high channel demand in hot cells by borrowing channels from suitable cold cells and by proper assignment (or, re-assignment) of the channels among the users. We also investigate possible ways of improving system capacity by rescheduling bandwidth in case of wireless multimedia traffic. In our proposed scheme, traffic using multiple channels releases one or more channels to increase the carried traffic or throughput in the system. Two orthogonal QoS parameters, called carried traffic and bandwidth degradation, are identified and a cost function describing the total revenue earned by the system from a bandwidth degradation and call admission policy, is formulated. A channel sharing scheme is proposed for …
Software reuse can occur in various places during the software development cycle. Reuse of existing source code is the most commonly practiced form of software reuse. One of the key requirements for software reuse is readability, thus the interest in the use of data abstraction, inheritance, modularity, and aspects of the visible portion of module specifications. This research analyzed the contents of software reuse libraries to answer the basic question of what makes a good reusable software component. The approach taken was to measure and analyze various software metrics as mapped to design characteristics. A related research question investigated the change in the design principles over time. This was measured by comparing sets of Ada reuse libraries categorized into two time periods. It was discovered that recently developed Ada reuse components scored better on readability than earlier developed components. A benefit of this research has been the development of a set of "design for reuse" guidelines. These guidelines address coding practices as well as design principles for an Ada implementation. C++ software reuse libraries were also analyzed to determine if design principles can be applied in a language independent fashion. This research used cyclomatic complexity metrics, software science metrics, and traditional static code metrics to measure design features. This research provides at least three original contributions. First it collects empirical data about existing reuse libraries. Second, it develops a readability measure for software libraries which can aid in comparing libraries. And third, this research developed a set of coding and design guidelines for developers of reusable software. Future research can investigate how design principles for C++ change over time. Another topic for research is the investigation of systems employing reused components to determine which libraries are more successfully used than others.
The primary purpose of this work is to study, implement and analyze the performance of parallel algorithms related to subsequence problems. The problems include string to string correction problem, to determine the longest common subsequence problem and solving the sum-range-product, 1 —D pattern matching, longest non-decreasing (non-increasing) (LNS) and maximum positive subsequence (MPS) problems. The work also includes studying the techniques and issues involved in developing parallel applications. These algorithms are implemented on the Sequent Multiprocessor System. The subsequence problems have been defined, along with performance metrics that are utilized. The sequential and parallel algorithms have been summarized. The implementation issues which arise in the process of developing parallel applications have been identified and studied.
Representing time has been considered a general problem for artificial intelligence research for many years. More recently, the question of representing time has become increasingly important in representing human decision making process through connectionist expert systems. Because most human behaviors unfold over time, any attempt to represent expert performance, without considering its temporal nature, can often lead to incorrect results. A temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems, has been introduced. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications. A temporal backpropagation algorithm which supports the model has been developed. The model along with the temporal backpropagation algorithm makes it extremely practical to define any artificial neural network application. Also, an approach that can be followed to decrease the memory space used by weight matrix has been introduced. The algorithm was tested using a medical connectionist expert system to show how best we describe not only the disease but also the entire course of the disease. The system, first, was trained using a pattern that was encoded from the expert system knowledge base rules. Following then, series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The first series of experiments was done to determine if the training process worked as predicted. In the second series of experiments, the weight matrix in the trained system was defined as a function of time intervals before presenting the system with the learned patterns. The result of the two experiments indicate that both approaches produce correct results. The only difference between the two results …
The physical limitations of uniprocessors and the real-time requirements of numerous practical applications have made parallel processing an essential technology in military, industry and scientific research. In this dissertation, we investigate parallelizations of three practical applications using three parallel machine models. The algorithms are: Finitely inductive (FI) sequence processing is a pattern recognition technique used in many fields. We first propose four parallel FI algorithms on the EREW PRAM. The time complexity of the parallel factoring and following by bucket packing is O(sk^2 n/p), and they are optimal under some conditions. The parallel factoring and following by hashing requires O(sk^2 n/p) time when uniform hash functions are used and log(p) ≤ k n/p and pm ≈ n. Their speedup is proportional to the number processors used. For these results, s is the number of levels, k is the size of the antecedents and n is the length of the input sequence and p is the number of processors. We also describe algorithms for raster/vector conversion based on the scan model to handle block-like connected components of arbitrary geometrical shapes with multi-level nested dough nuts for the IES (image exploitation system). Both the parallel raster-to-vector algorithm and parallel vector-to-raster algorithm require O(log(n2)) or O(log2(n2)) time (depending on the sorting algorithms used) for images of size n2 using p = n2 processors. Not only is the DWT (discrete wavelet transforms) useful in data compression, but also has it potentials in signal processing, image processing, and graphics. Therefore, it is of great importance to investigate efficient parallelizations of the wavelet transforms. The time complexity of the parallel forward DWT on the parallel virtual machine with linear processor organization is O(((so+s1)mn)/p), where s0 and s1 are the lengths of the filters and p is the number of processors used. The time complexity of the …
The efficacy of a machine learning technique is domain dependent. Some machine learning techniques work very well for certain domains but are ill-suited for other domains. One area that is of real-world concern is the flexibility with which machine learning techniques can adapt to dynamic domains. Currently, there are no known reports of any system that can learn dynamic domains, short of starting over (i.e., re-running the program). Starting over is neither time nor cost efficient for real-world production environments. This dissertation studied a method, referred to as Experience Based Learning (EBL), that attempts to deal with conditions related to learning dynamic domains. EBL is an extension of Instance Based Learning methods. The hypothesis of the study related to this research was that the EBL method would automatically adjust to domain changes and still provide classification accuracy similar to methods that require starting over. To test this hypothesis, twelve widely studied machine learning datasets were used. A dynamic domain was simulated by presenting these datasets in an uninterrupted cycle of train, test, and retrain. The order of the twelve datasets and the order of records within each dataset were randomized to control for order biases in each of ten runs. As a result, these methods provided datasets that represent extreme levels of domain change. Using the above datasets, EBL's mean classification accuracies for each dataset were compared to the published static domain results of other machine learning systems. The results indicated that the EBL's system performance was not statistically different (p>0.30) from the other machine learning methods. These results indicate that the EBL system is able to adjust to an extreme level of domain change and yet produce satisfactory results. This finding supports the use of the EBL method in real-world environments that incur rapid changes to both variables and …
The scope of the present dissertation is a deep lossy compression of still and moving grayscale pictures while maintaining their fidelity, with a specific goal of creating a working prototype of a software system for use in low bandwidth transmission of still satellite imagery and weather briefings with the best preservation of features considered important by the end user.
The device optimization is a very important element in semiconductor technology advancement. Its objective is to find a design point for a semiconductor device so that the optimized design goal meets all specified constraints. As in other engineering fields, a nonlinear optimizer is often used for design optimization. One major drawback of using a nonlinear optimizer is that it can only partially explore the design space and return a local optimal solution. This dissertation provides an adaptive optimization design methodology to allow the designer to explore the design space and obtain a globally optimal solution. One key element of our method is to quickly compute the set of all feasible solutions, also called the acceptability region. We described a polytope-based representation for the acceptability region and an adaptive linearization technique for device performance model approximation. These efficiency enhancements have enabled significant speed-up in estimating acceptability regions and allow acceptability regions to be estimated for a larger class of device design tasks. Our linearization technique also provides an efficient mechanism to guarantee the global accuracy of the computed acceptability region. To visualize the acceptability region, we study the orthogonal projection of high-dimensional convex polytopes and propose an output sensitive algorithm for projecting polytopes into two dimensions.
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