This directory is created automatically and some papers may be mislabeled. Only document within the CiteSeer database are listed. The directory is intended to provide entry points for browsing the database and is not intended to be authoritative. Papers may not appear in all relevant categories. For example, papers in a sub-category may not appear in higher level categories.
373 Complements to 'Pattern Recognition and Neural Networks' - Ripley (1996)(Correct)
Introduction Page 4: The book by Przytula & Prasanna (1993) discusses in detail the parallel implementation of neural networks. Page 16: Langley (1996) provides a book-length introduction to one viewp... / a more recent method in convex optimization. They also show that every br add a penalty to the optimization to encourage exploring broad
132 Noise Strategies for Improving Local Search - Selman, Kautz, Cohen (1994)(Correct)
It has recently been shown that local search is surprisingly
good at finding satisfying assignments for
certain computationally hard classes of CNF formulas.
The performance of basic local search meth... / successfully applied to many optimization problems. Hansen and Jaumard br approach in combinatorial optimization of terminating the search when
131 BIRCH: An Efficient Data Clustering Method for Very Large Databases - Zhang, Ramakrishnan, Livny (1996)(Correct)
Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely studied problems in this area is the identification of clusters, or densely populated... / is a nonconvex discrete KR optimization problem. Due to an abundance br extremely small. Iterative optimization IO DH KR starts with
128 Performance of Dynamic Load Balancing Algorithms for Unstructured.. - Williams (1991)(Correct)
If a finite element mesh has a sufficiently regular structure, it is easy to decide in advance how to distribute the mesh among the processors of a distributed-memory parallel processor, but if the me... / than many small messages. This optimization problem for the mesh br types depending on when the optimization is made and whether the cost
79 A New Method for Mapping Optimization Problems onto Neural Networks - Peterson, Söderberg (1989)(Correct)
A novel modified method for obtaining approximate solutions to difficult optimization problems within the neural network paradigm is presented. We consider the graph partition and the travelling sal... / A New Method for Mapping Optimization Problems onto Neural Networks br solutions to difficult optimization problems within the neural
76 Local Search Strategies for Satisfiability Testing - Selman (1996)(Correct)
It has recently been shown that local search is surprisingly good at
finding satisfying assignments for certain classes of CNF formulas (Selman
et al. 1992). In this paper we demonstrate that the pow... / successfully applied to many optimization problems. Hansen and Jaumard br approach in combinatorial optimization of terminating the search when
73 The Reactive Tabu Search - Battiti (1994)(Correct)
this paper the concept of chaotic attractor is used only as an example of a dynamic behavior that could affect the search process, we summarize the main characteristics and refer to [13] for a detaile... / an algorithm for combinatorial optimization where an explicit check for br scheme for combinatorial optimization that combines a hill-climbing
63 A Review of Evolutionary Artificial Neural Networks - Yao (1993)(Correct)
Research on potential interactions between connectionist learning systems, i.e., artificial neural networks (ANNs), and evolutionary search procedures, like genetic algorithms (GAs), has attracted a l... / the rapid generation and optimisation of tightly pruned interesting br The shift from the direct optimisation of connectivity patterns to the
61 An Evolutionary Algorithm that Constructs Recurrent Neural Networks - Angeline, Saunders, Pollack (1994)(Correct)
Standard methods for inducing both the structure and weight values of recurrent neural
networks fit an assumed class of architectures to every task. This simplification is necessary
because the intera... / to perform well at function optimization. Section argues that this br Genetic Algorithms in Search Optimization and Machine Learning. Addison
57 Improving Regression Estimation: Averaging Methods for Variance.. - Perrone (1993)(Correct)
Perrone, M. P. and N. Intrator (1992) Unsupervised Splitting Rules for Neural Tree Classifiers.
Proceedings of the International Joint Conference on Neural Networks pp. III:820-825.
Perrone, M. P.,... / to General Convex Measure Optimization by Michael Peter Perrone B. br Extensions to Convex Optimization . Introduction
55 A Multiscale Random Field Model for Bayesian Image Segmentation - Bouman, Shapiro (1996)(Correct)
Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). While this approach performs well, it has a number of dis... / criteria results in a series of optimization steps going from coarse to br This is done by solving the optimization problem x arg min x
50 The Geometry Of Algorithms With Orthogonality Constraints - Edelman, Arias, Smith (1998)(Correct)
In this paper we develop new Newton and conjugate gradient algorithms on the
Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such
areas as the symmetric eige... / quotient iteration eigenvalue optimization sequential quadratic br ideas from different areas. The optimization community has long recognized
47 Competitive Environments Evolve Better Solutions for Complex Tasks - Angeline, Pollack (1993)(Correct)
In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective measure. Such objectivity entails... / solutions for many simple optimization problems it is often br Genetic Algorithms in Search Optimization and Machine Learning
47 A graduated assignment algorithm for graph matching - Gold, Rangarajan (1996)(Correct)
A graduated assignment algorithm for graph matching is presented which is
fast and accurate even in the presence of high noise. By combining graduated
non-convexity (deterministic annealing), two-way ... / approach employs nonlinear optimization methods or heuristic br search methods. Other nonlinear optimization approaches are neural
47 An Empirical Study of Algorithms for Point-Feature Label Placement - Christensen, Marks, Shieber (1995)(Correct)
A major factor affecting the clarity of graphical displays that include text labels is the degree to which labels obscure display
features (including other labels) as a result of spatial overlap. Poin... / as a type of combinatorial optimization problem. Complexity analysis br be thought of as a combinatorial optimization problem. Like all such
42 Feature Subset Selection Using A Genetic Algorithm - Yang (1997)(Correct)
this paper. See [1] for a more complete list of references). Some of these involve searching for an optimal subset of features based on some criteria of interest. Feature weighting is a variant of fea... / an instance of a multi-criteria optimization problem. The multiple criteria br approach for multicriteria optimization. This paper explores a
38 Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL.. - Zhu (1996)(Correct)
We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criteri... / and iv Global optimization approaches based on energy br algorithm from a global optimization criterion. Then we show how
38 A Comprehensive Survey of Evolutionary-Based Multiobjective.. - Coello (1998)(Correct)
This paper presents a critical review of the most important
evolutionary-based multiobjective optimization techniques developed
over the years, emphasizing the importance of analyzing their Operatio... / Multiobjective Optimization Techniques Carlos A. Coello br multiobjective optimization techniques developed over the
38 Bead: Explorations in Information Visualization - Chalmers, Chitson (1992)(Correct)
We describe work on the visualization of bibliographic data
and, to aid in this task, the application of numerical techniques
for multidimensional scaling.
Many areas of scientific research involve co... / retrieval numerical optimization and computational physics. br the configuration task as an optimization problem. This has allowed the
38 Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL.. - Zhu (1995)(Correct)
We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL(Minimum... / and iv Global optimization approaches based on energy br derive our theory from the MDL optimization criterion then we show how
35 On the Applications of Harmonic Functions to Robotics - Connolly, Grupen (1993)(Correct)
Harmonic functions are solutions to Laplace's equation. Such functions can be used to advantage for potential-field path planning, since they do not exhibit spurious local minima. Harmonic functions a... / they do not exhibit spurious local minima. Harmonic functions are shown br the spontaneous creation of minima other than the goal. The robot
33 A New Merit Function For Nonlinear Complementarity Problems And A.. - Facchinei, Soares (1997)(Correct)
We investigate the properties of a new merit function which allows us to reduce
a nonlinear complementarity problem to an unconstrained global minimization one. Assuming
that the complementarity pro... / showed to be relevant to optimization algorithms. Recently the br play an important role in many optimization problems. Under very mild
30 Object-Centered Surface Reconstruction: Combining Multi-Image Stereo.. - Fua, Leclerc (1995)(Correct)
Our goal is to reconstruct both the shape and reflectance properties of surfaces from multiple
images. We argue that an object-centered representation is most appropriate for this purpose
because it n... / described below in which the optimization procedure dynamically adapts br a computationally effective optimization procedure and have
30 Autocalibration from Planar Scenes - Triggs (1998)(Correct)
This paper describes a theory and a practical algorithm for the autocalibration of a moving projective camera,
from m 5 views of a planar scene. The unknown camera calibration, and (up to scale) the ... / model to initialize the optimization from a minimal algebraic br to use except that constrained optimization is required to handle the rank
30 An experimental comparison of several clustering and initialization.. - Melia (1998)(Correct)
Data clustering is an important technique for exploratory data analysis, and has been studied for several years. It has been shown to be useful in many practical domains such as data classification ... / it is a nonconvex discrete optimization problem. Due to an br vectors during the codebook optimizations at all the codebook size
27 Genetic Local Search for the TSP: New Results - Merz, Freisleben (1997)(Correct)
The combination of local search heuristics and genetic algorithms has been shown to be an effective approach for finding near-optimum solutions to the traveling salesman problem. In this paper, previo... / the best-known combinatorial optimization problems. It has attracted br Contest on Evolutionary Optimization held as part of the
27 Neural Networks for Optimal Approximation of Smooth and Analytic.. - Mhaskar (1996)(Correct)
We prove that neural networks with a single hidden layer are capable of providing an optimal
order of approximation for functions assumed to possess a given number of derivatives, if the activation
... / are extremely simple use no optimization and avoid all the problems br associated with the classical optimization-based training paradigms such
26 Evolutionary Algorithms for Neural Network Design and Training - Branke (1995)(Correct)
Neural networks and genetic algorithms are two relatively young research areas
that were subject to a steadily growing interest during the past years. Both models
are inspired by nature, but whereas n... / of a network can be seen as an optimization process with the goal to find br algorithms compared to other optimization methods is that heuristics can
26 MIMIC: Finding Optima by Estimating Probability Densities - De Bonet, Isbell, Jr., Viola (1996)(Correct)
In many optimization problems, the structure of solutions reflects complex relationships between the different input parameters. For example, experience may tell us that certain parameters are closely... / MA Abstract In many optimization problems the structure of br the global structure of the optimization landscape. A novel and
26 A New Approach to Effective Circuit Clustering - Hagen, Kahng (1992)(Correct)
The complexity of next-generation VLSI systems
will exceed the capabilities of top-down layout synthesis
algorithms, particularly in netlist partitioning and
module placement. Bottom-up clustering is ... / becomes tractable to existing optimization methods. In this paper we br interchange or quadratic optimization via relaxation or
24 Evolving Artificial Neural Networks - Yao (1999)(Correct)
Learning and evolution are two fundamental forms of adaptation. There has been a great
interest in combining learning and evolution with artificial neural networks (ANNs) in recent
years. This paper (... / ANNs and EAs for combinatorial optimization will be mentioned but not br data. A gradient descent-based optimization algorithm such as
24 Evolutionary Design of Neural Architectures - A Preliminary Taxonomy.. - Balakrishnan, Honavar (1995)(Correct)
This report briefly motivates current research on evolutionary design of neural
architectures (EDNA) and presents a short overview of major research issues in
this area. It also includes a preliminary... / a challenging multi-criterion optimization problem namely that of br D. Genetic Algorithms in Search Optimization and Machine Learning. Reading
21 A Discrete Lagrangian-Based Global-Search Method for Solving.. - Wah, Shang (1998)(Correct)
Satisfiability is a class of NP-complete problems that model a wide range of real-world applications. These problems are difficult to solve because they have many local minima in their search space, o... / constrained or unconstrained optimization problems. In Section we br as a discrete constrained optimization problem with a goal of
21 A Genetic Local Search Algorithm for Solving Symmetric and Asymmetric .. - Freisleben, Merz (1996)(Correct)
The combination of local search heuristics and genetic algorithms is a promising approach for finding nearoptimum solutions to the traveling salesman problem (TSP). In this paper, an approach is prese... / testbed for combinatorial optimization methods which attempt to find br at various steps of the GA optimization process. The proposed
20 A Fast Adaptive Layout Algorithm for Undirected Graphs (Extended.. - Frick, Ludwig, Mehldau (1994)(Correct)
and System Demonstration)
Arne Frick
?
, Andreas Ludwig, Heiko Mehldau
Universitat Karlsruhe, Fakultat fur Informatik, D-76128 Karlsruhe, Germany
Abstract. We present a randomized adaptive layout a... / art. Even more so simultaneous optimization for several criteria can br to the simultaneous optimization problem in Sect. . We
20 Subdivision Direction Selection In Interval Methods For Global.. - Csendes (1997)(Correct)
The role of the interval subdivision selection rule is investigated in branch-and-bound
algorithms for global optimization. The class of rules that allow convergence for the model algorithm is
chara... / In Interval Methods For Global Optimization T. Csendes Y And D. br algorithms for global optimization. The class of rules that allow
20 Learning long-term dependencies in NARX recurrent neural networks - Lin, Horne, Tino, Giles (1996)(Correct)
It has recently been shown that gradient-descent learning algorithms for recurrent neural
networks can perform poorly on tasks that involve long--term dependencies, i.e. those problems
for which the d... / in favor of alternative optimization methods However the br described in This problem is a minimal task designed as a test that
19 Comparative Bibliography of Ontogenic Neural Networks - Fiesler (1994)(Correct)
This document will be published in: Proceedings of the International Conference on
Artificial Neural Networks (ICANN '94).
until it is large enough to handle the problem (or to eliminate possible lo... / generalization implementation optimization size and or execution speed br speed and the avoidance of local minima. Possible topology modifications
19 Initialization of Iterative Refinement Clustering Algorithms - Fayyad, Reina, Bradley (1998)(Correct)
Iterative refinement clustering algorithms (e.g. K-Means,
EM) converge to one of numerous local minima. It is known
that they are especially sensitive to initial conditions. We
present a procedure for... / pattern recognition DH F optimization BMS SI and statistics br given as input. The clustering optimization problem is that of finding
19 Dynamic Euclidean Minimum Spanning Trees and Extrema of Binary.. - Eppstein (1995)(Correct)
We maintain the minimum spanning tree of a point set in the plane, subject to point insertions and deletions, in amortized time O(n 1/2 log 2 n) per update operation. We reduce the problem to maintain... / been studied for many geometric optimization problems including closest br and many similar geometric optimization problems. Dobkin and Suri call
18 Search-Intensive Concept Induction - Giordana (1995)(Correct)
This paper describes REGAL, a distributed genetic algorithm-based system, designed
for learning First Order Logic concept descriptions from examples. The system is a
hybrid between Pittsburgh's and Mi... / successfully applied mainly to optimization problems also machine br functions to a set of benchmark optimization problems Deb and Goldberg
18 On The Problem Of Local Minima In Backpropagation - Gori, Tesi (1992)(Correct)
Supervised Learning in Multi-Layered Neural Networks (MLNs) has been recently
proposed through the well-known Backpropagation algorithm. This is a gradient method
which can get stuck in local minima, ... / gradient methods for nonlinear optimization Proceedings of the first br On The Problem Of Local Minima In Backpropagation M. Gori And A.
17 Using Genetic Algorithms for Robot Motion Planning - Ahuactzin, Talbi, Bessiere, Mazer (1992)(Correct)
We present an ongoing research work on robot motion planning using genetic algorithms. Our goal is to use this technique to build fast motion planners for robot with six or more degree of freedom. A... / problem can be expressed as an optimization problem and thus solved with a br Genetic algorithms in search optimization and machine learning The
17 Design Of Multi-Dimensional Derivative Filters - Simoncelli (1994)(Correct)
Many multi-dimensional signal processing problems require the computation of signal gradients or directional derivatives. Traditional derivative estimates based on adjacent or central differences are ... / analytically and avoid complex optimization procedures that may get stuck br methods with local optimization. IEEE Pat. Anal. Mach.
17 Learning Rate Schedules For Faster Stochastic Gradient Search - Darken, Chang, Moody (1992)(Correct)
Stochastic gradient descent is a general algorithm that includes LMS, on-line backpropagation, and adaptive k-means clustering as special cases. The standard choices of the learning rate j (both ada... / techniques for solving optimization problems Ljung and Soderstrom br of ff by performing a second optimization. However here we take a more
17 Conformal curvature flows: from phase transitions to active vision - Kichenassamy, Kumar (1995)(Correct)
In this paper, we analyze geometric active contour models from a curve evolution
point of view and propose some modifications based on gradient flows relative to certain
new feature-based Riemannian m... / briefly sketch the energy based optimization approach to deformable br constrained version of the given optimization problem and so derive a
17 Evolutionary Artificial Neural Networks - Yao (1993)(Correct)
Evolutionary Artificial Neural Networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search procedures, such as genetic algorithms (GAs). This paper... / facilitate rapid generation and optimisation of tightly pruned interesting br The shift from the direct optimisation of architectures to the
16 Global Optimization for Neural Network Training - Shang (1996)(Correct)
In this paper, we study various supervised learning methods for training feed-forward neural networks.
In general, such learning can be considered as a nonlinear global optimization problem in which t... / Global Optimization for Neural Network Training br considered as a nonlinear global optimization problem in which the goal is
16 Refining Initial Points for K-Means Clustering - Bradley, Fayyad (1998)(Correct)
Practical approaches to clustering use an iterative
procedure (e.g. K-Means, EM) which converges
to one of numerous local minima. It is known
that these iterative techniques are especially
sensitive t... / pattern recognition DH F optimization BMS SI and statistics br given as input. The clustering optimization problem is that of finding
16 Weak Sharp Minima In Mathematical Programming - Burke, Ferris (1993)(Correct)
The notion of a sharp, or strongly unique, minimum is extended to include the possibility of a nonunique solution set. These minima will be called weak sharp minima. Conditions necessary for the sol... / be applied to convex composite optimization problems to establish the br this we consider the related optimization problem minimize hx Mx
16 A Continuous Approach to Inductive Inference - Kamath (1992)(Correct)
In this paper we describe an interior point mathematical programming approach to inductive
inference. We list several versions of this problem and study in detail the formulation based
on hidden Boole... / inference is in fact an optimization problem where one wants to br are many ways to formalize this optimization problem. In this paper we
16 Adaptive Global Optimization with Local Search - Hart (1994)(Correct)
xiv
I Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1
A. Global Optimization : : : : : : : : ... / San Diego Adaptive Global Optimization With Local Search A br A. Global Optimization
15 New Genetic Local Search Operators for the Traveling Salesman Problem - Freisleben, Merz (1996)(Correct)
In this paper, an approach is presented to incorporate problem
specific knowledge into a genetic algorithm which is used to compute
near-optimum solutions to traveling salesman problems (TSP). The a... / NP-hard combinatorial optimization problem in which a br methods useful in a variety of optimization problems such as simulated
15 A hierarchical statistical framework for the segmentation of.. - Kervrann, Heitz (1993)(Correct)
In this paper, we propose a new statistical framework for modeling and extracting
2D moving deformable objects from image sequences. The object representation
relies on a hierarchical description of... / procedures. The use of global optimization algorithms yields robust and br bayesian estimation global optimization motion segmentation
15 Fast Approximate Energy Minimization via Graph Cuts - Boykov, Veksler (1999)(Correct)
Many tasks in computer vision involve assigning a label (such as disparity) to every
pixel. A common constraint is that the labels should vary smoothly almost everywhere.
These tasks are naturally sta... / on graph cuts as a powerful optimization technique. An important br to solving a known combinatorial optimization problem called the minimum
14 Registering Multiview Range Data to Create 3D Computer Objects - Blais, Levine (1993)(Correct)
This research deals with the problem of range image registration for the purpose of building
surface models of three-dimensional objects. The registration task involves finding the translation
and rot... / the registration task as an optimization problem. We define a function br space. A stochastic optimization technique Very Fast Simulated
14 Bilevel and Multilevel Programming: A Bibliography Review - Vicente, Calamai (1994)(Correct)
This paper contains a bibliography of all references central to bilevel and multilevel
programming that the authors know of. It should be regarded as a dynamic
and permanent contribution since, all... / problems hierarchical optimization minimax problems. AMS br historical notes Multilevel optimization problems are mathematical
14 Bayesian Training of Backpropagation Networks by the Hybrid Monte.. - Neal (1993)(Correct)
It is shown that Bayesian training of backpropagation neural networks can feasibly
be performed by the "Hybrid Monte Carlo" method. This approach allows the true predictive
distribution for a test c... / conventional training is an optimization problem Bayesian training and br many modes has been found by an optimization procedure If one also
14 Isotropic Effective Energy Simulated Annealing Searches for Low.. - Coleman, Shalloway, Wu (1993)(Correct)
The search for low energy states of molecular clusters is associated with
the study of molecular conformation and especially protein folding. This paper describes
a new global minimization algorithm... / in the area of numerical optimization. A number of approaches br to global and combinatorial optimization. Since then the algorithm has
14 A Motion Planning Based Approach for Inverse Kinematics of Redundant.. - Ahuactzin, al. (1997)(Correct)
We propose a new approach to solving the point-topoint
inverse kinematics problem for highly redundant
manipulators. It is inspired by recent motion planning
research and explicitly takes into account... / path is found more elaborate optimization criteria can then be used to br inverse kinematic problem as an optimization problem as is normally done in
14 A New Evolutionary System for Evolving Artificial Neural Networks - Yao (1996)(Correct)
This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP) [... / the Baldwin effect and function optimization in Parallel Problem br Y. Shang and B. Wah Global optimization for neural network training
13 The Dynamics of a Genetic Algorithm under Stabilizing Selection - Rattray (1996)(Correct)
A formalism recently introduced by Prugel-Bennett and Shapiro uses the methods of statistical mechanics to model the dynamics of genetic algorithms. To be of more general interest this formalism must ... / which is a combinatorial optimization problem with a strongly br to a harder combinatorial optimization problem subset sum. Although
13 Cost Versus Distance In the Traveling Salesman Problem - Boese (1995)(Correct)
This paper studies the distribution of good solutions for the traveling salesman problem (TSP) on a well-known 532-city instance that has been solved optimally by Padberg and Rinaldi [16]. For each of... / multi-start strategy for global optimization called Adaptive Multi-Start br to combinatorial global optimization problems. In global
13 Local Verification of Global Integrity Constraints in Distributed.. - Gupta, Widom (1993)(Correct)
We present an optimization for integrity constraint
verification in distributed databases. The optimization
allows a global constraint, i.e. a constraint spanning
multiple databases, to be verified by... / Abstract We present an optimization for integrity constraint br in distributed databases. The optimization allows a global constraint
13 Algebraic Transformations of Objective Functions - Mjolsness, Garrett (1994)(Correct)
Many neural networks can be derived as optimization dynamics for suitable
objective functions. We show that such networks can be designed by repeated
transformations of one objective into another wit... / networks can be derived as optimization dynamics for suitable br trajectory followed during optimization. We do not require that the
13 An Improved Worst-Case to Average-Case Connection for Lattice.. - Cai, Nerurkar (1997)(Correct)
We improve a connection of the worst-case complexity and the average-case complexity of some well-known lattice problems. This fascinating connection was first discovered by Ajtai [1] in 1996. We impr... / approximation and combinatorial optimization integer programming br Algorithms and Combinatorial Optimization. Springer Verlag .
13 New NCP-Functions and Their Properties - Kanzow, Yamashita, Fukushima (1997)(Correct)
Recently, Luo and Tseng proposed a class of merit functions for the nonlinear complementarity problem (NCP) and showed that it enjoys several interesting properties under some assumptions. In this p... / merit function unconstrained optimization reformulation error bound br can be cast as the unconstrained optimization problem with the objective
13 Object Pose from 2-D to 3-D Point and Line Correspondences - Phong, Horaud, Yassine, Tao (1995)(Correct)
In this paper we present a method for optimally estimating the rotation and translation
between a camera and a 3-D object from point and/or line correspondences. First we devise an
error function and ... / aspects of a trust-region optimization method. This method compares br Levenberg-Marquardt non-linear optimization method. Finally we present some
13 A Fast Stochastic Error-Descent Algorithm for Supervised Learning and .. - Cauwenberghs (1993)(Correct)
A parallel stochastic algorithm is investigated for error-descent
learning and optimization in deterministic networks of arbitrary
topology. No explicit information about internal network structure
... / for Supervised Learning and Optimization Gert Cauwenberghs br for error-descent learning and optimization in deterministic networks of
13 Algorithmic Geometry of Numbers - Kannan (1987)(Correct)
this article - Algorithmic Geometry of Numbers. The fundamental basis reduction
algorithm of Lov'asz which first appeared in Lenstra, Lenstra, Lov'asz [46] was used in Lenstra's
algorithm for integer ... / programming and other discrete optimization problems which seem inherently br The integer programming optimization problem is the problem of
13 Supply and Threshold Voltage Scaling for Low Power CMOS - Gonzalez (1997)(Correct)
This paper investigates the effect of lowering the supply and threshold voltages on the energy efficiency of CMOS circuits. Using a first-order model of the energy and delay of a CMOS circuit, we show... / at both energy and delay during optimization and use the energy-delay br thus one wants to operate near the minima but gates at this point are
13 A Parallel Build-Up Algorithm for Global Energy Minimizations of.. - Coleman, Shalloway, Wu (1993)(Correct)
This work studies the build-up method for the global minimization problem
for molecular conformation, especially protein folding. The problem is hard to solve for
large molecules using general minim... / in the area of numerical optimization. Many approaches have been br Smith Parallel Global Optimization Numerical Methods Dynamic
12 Input/Output HMMs for Sequence Processing - Bengio, Frasconi (1995)(Correct)
We consider problems of sequence processing and propose a solution based on a discrete state model in order to represent past context. We introduce a recurrent connectionist architecture having a modu... / supervised learning as an optimization problem in the joint space of br obtained by maximizing The optimization problem arising from this
12 A Review Of Techniques In The Verified Solution Of Constrained Global .. - Kearfott (1996)(Correct)
Elements and techniques of state-of-the-art automatically verified constrained global
optimization algorithms are reviewed, including a description of ways of rigorously
verifying feasibility for equa... / Solution Of Constrained Global Optimization Problems R. Baker Kearfott br verified constrained global optimization algorithms are reviewed
12 Conformational analysis of molecular chains using Nano-Kinematics - Manocha (1995)(Correct)
We present algorithms for 3-D manipulation and conformational analysis of molecular
chains, when bond length, bond angles and related dihedral angles remain fixed. These algorithms
are useful for lo... / are based on stochastic optimization and Monte Carlo analysis br on numerical techniques like optimization or Newton's method to the
12 Analysis of Heuristic Methods for Partial Constraint Satisfaction.. - Wallace (1996)(Correct)
Problems that do not have complete solutions occur in many
areas of application of constraint solving. Heuristic repair methods that
have been used successfully on complete CSPs can also be used on ... / MAX-CSPs. Since these are optimization problems the basic question is br L. A. McGeoch and C. Shevon. Optimization by simulated annealing An
12 Training Neural Nets with the Reactive Tabu Search - Battiti, Tecchiolli (1995)(Correct)
In this paper the task of training sub-symbolic systems is considered as a combinatorial optimization problem and solved with the heuristic scheme of the Reactive Tabu Search (RTS) proposed by the aut... / is considered as a combinatorial optimization problem and solved with the br Tabu Search. An iterative optimization process based on a modified
12 Differential Evolution - A simple and efficient adaptive scheme for.. - Storn, Price (1995)(Correct)
A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it wi... / adaptive scheme for global optimization over continuous spaces by br Problems which involve global optimization over continuous spaces are
12 Reactive Search: Toward Self-Tuning Heuristics - Battiti (1996)(Correct)
Local (neighborhood) search can be guided to go beyond local minima through metaheuristic
methods that use the information obtained in the previous part of the run. The
Reactive Search (RS) method pro... / in heuristics for discrete optimization problems. RS belongs to the br basin lose any interest for optimization. The search should avoid
12 Preference Logic Programming - Govindarajan (1995)(Correct)
This research is concerned with the semantics, applications, and implementation of a declarative
language for specifying constraint optimization and relaxation problems. These problems arise in
divers... / for specifying constraint optimization and relaxation problems. These br how to formulate constraint optimization problems as well as heuristics
12 Alopex: A Correlation-Based Learning Algorithm for Feed-Forward and.. - Unnikrishnan, Venugopal (1994)(Correct)
e present a learning algorithm for neural networks, called Alopex. Instead of
a
error gradient, Alopex uses local correlations between changes in individual weights
nd changes in the global error mea... / for solving combinatorial optimization problems Harth Pandya br neural network is treated as an optimization problem. The objec- a
12 Global Optimization For The Phase Stability Problem - M.Mcdonald(Correct)
The Gibbs tangent plane criterion has become an important tool in determining the quality
of obtained solutions to the phase and chemical equilibrium problem. The ability to
determine if a postulated ... / Global Optimization For The Phase Stability br The advantage of a global optimization approach is that if a
11 Acyclic Multi-Way Partitioning of Boolean Networks - Jason Cong (1994)(Correct)
Acyclic partitioning on combinational boolean networks
has wide range of applications, from multiple
FPGA chip partitioning to parallel circuit simulation. In
this paper, we present two efficient algo... / final design for performance optimization. Partitioning based logic br also supports a number of optimizations to reduce both checkpointing
11 Trace-Based Methods for Solving Nonlinear Global Optimization and.. - Wah, Chang (1996)(Correct)
In this paper we present a method called NOVEL (Nonlinear Optimization via
External Lead) for solving continuous and discrete global optimization problems. NOVEL addresses
the balance between global... / Journal of Global Optimization - c fl Kluwer br for Solving Nonlinear Global Optimization and Satisfiability Problems
11 A Local Search Template - Vaessens, Aarts, Lenstra (1992)(Correct)
A template is presented that captures a vast majority of the local
search algorithms proposed in the literature, including iterative improvement,
simulated annealing, threshold accepting, tabu search,... / approximate solutions to hard optimization problems. The basic idea is to br Preliminaries An optimization problem is either a
11 A Genetic Local Search Approach to the Quadratic Assignment Problem - Merz, Freisleben (1997)(Correct)
Augmenting genetic algorithms with local search
heuristics is a promising approach to the solution
of combinatorial optimization problems. In
this paper, a genetic local search approach to
the quadrat... / the solution of combinatorial optimization problems. In this paper a br Compared to other combinatorial optimization problems such as the traveling
11 Constructive Feedforward Neural Networks for Regression Problems: A.. - Kwok, Yeung (1995)(Correct)
In this paper, we review the procedures for constructing feedforward neural networks in regression problems. While standard back-propagation performs gradient descent only in the weight space of a net... / two general approaches to this optimization problem. One involves using a br can be performed using various optimization algorithms which will be
11 Path Planning Using Lazy PRM - Bohlin, Kavraki (2000)(Correct)
This paper describes a new approach to probabilistic roadmap planners (PRMs). The overall theme of the algorithm, called Lazy PRM, is to minimize the number of collision checks performed during planni... / paths as follows. Genetic optimization is used to search for a br and random walks to escape local minima. Another interesting approach is
11 Coupled Geodesic Active Regions for Image Segmentation: A Level Set.. - Paragios, Deriche (1999)(Correct)
This paper presents a novel variational method for image segmentation
that unifies boundary and region-based information sources
under the Geodesic Active Region framework. A statistical analysis ... / hA and hB Then the optimization procedure refers to a frame br modules and connects the optimization procedure with the the curve
11 A Semismooth Equation Approach To The Solution Of Nonlinear.. - De Luca, Facchinei, Kanzow (1995)(Correct)
In this paper we present a new algorithm for the solution of nonlinear
complementarity problems. The algorithm is based on a semismooth equation reformulation
of the complementarity problem. We expl... / shown to be relevant to optimization algorithms. Recently the br D.P. Bertsekas Constrained Optimization and Lagrange Multiplier
10 A General Framework for Vertex Orderings, With Applications to.. - Alpert And (1996)(Correct)
Vertex orderings have been successfully applied to problems in netlist clustering, for system partitioning
and layout [2] [20]. We present a vertex ordering construction that encompasses most reasonab... / for comparison rather than optimization of clustering solutions. br and Lengauer that good local minima in the flattened instance are
10 Blind Separation of Convolutive Mixtures and an Application in.. - Ehlers, Schuster (1997)(Correct)
In this paper we propose a two-step-algorithm for
the blind separation of convolutive mixtures. We show that
its application to automatic speech recognition in a noisy
environment yields good results.... / proposed in - because optimization procedures of this kind are br . Gunter Dueck New Optimization Heuristics Journal of
10 Solving Problems with Hard and Soft Constraints Using a Stochastic.. - Jiang (1995)(Correct)
Stochastic local search is an effective technique for solving certain classes
of large, hard propositional satisfiability problems, including propositional encodings
of problems such as circuit synthe... / possible MAX-SAT encodings of an optimization problem are equally good. For br Thus we turned a method for optimization problems into one for decision
10 Fast Hierarchical Clustering and Other Applications of Dynamic.. - Eppstein (1998)(Correct)
We develop data structures for dynamic closest pair problems with arbitrary (not necessarily geometric) distance functions, based on a technique previously used by the author for Euclidean closest pai... / basis computation and local optimization methods. Although dynamic br cluster centroid. . Local Optimization. Local search procedures such
10 Reevaluating Genetic Algorithm Performance under Coordinate Rotation.. - Salomon (1995)(Correct)
This work analyzes some concepts of genetic algorithms and explains why they may be applied with success to some problems in function optimization. In addition to other performance properties, it has ... / to some problems in function optimization. In addition to other br cause a failure of the optimization procedure even though the
10 A Smoothing Method For Mathematical Programs With Equilibrium.. - Facchinei, Jiang, Qi (1996)(Correct)
The mathematical program with equilibrium constraints (MPEC) is an optimization
problem with variational inequality constraints. MPEC problems include bilevel
programming problems as a particular ca... / constraints MPEC is an optimization problem with variational br equivalent one-level nonsmooth optimization problem. Then a sequence of
10 Activity Analysis: The Qualitative Analysis of Stationary Points for.. - Williams (1994)(Correct)
We present a theory of a modeler's problem decomposition skills in the context of optimal reasoning --- the use of qualitative modeling to strategically guide numerical explorations of objective space... / and non-linear constrained optimization problems and easily br approaches of continuous optimization -combined with the
9 Improving Convergence and Solution Quality of Hopfield-Type Neural.. - Li (1996)(Correct)
Hopfield-type networks convert a combinatorial optimization to a constrained real optimization and solve the latter
using the penalty method. There is a dilemma with such networks: When tuned to produ... / networks convert a combinatorial optimization to a constrained real br to a constrained real optimization and solve the latter using the
9 Using Prediction to Improve Combinatorial Optimization Search - Boyan, Moore (1997)(Correct)
To appear in AISTATS-97
This paper describes a statistical approach to improving the performance
of stochastic search algorithms for optimization. Given a
search algorithm A, we learn to predict the o... / to Improve Combinatorial Optimization Search Justin A. Boyan and br stochastic search algorithms for optimization. Given a search algorithm A
9 Evolution in a Rugged Fitness Landscape - Flyvbjerg, Lautrup (1992)(Correct)
Kauffman's NK-model for genetic evolution and adaption is analysed for K =
N \Gamma 1. In this case it describes adaptive walks on random fitness landscapes, and
its dynamics is equivalent to the Met... / they crop up in combinatorial optimization problems and in the training br evolution to attack these hard optimization problems In the present
9 Learning without Local Minima in Radial Basis Function Networks - Bianchini, Frasconi, Gori (1995)(Correct)
Learning from examples plays a central role in artificial neural networks (ANN). However, the success of many learning schemes is not guaranteed, since algorithms like Backpropagation (BP) may get stu... / essentially deal with the optimization of functions that in pattern br The rich literature on optimization algorithms still has to be
9 A Parallel Simulated Annealing Algorithm for Generating 3D Layouts of .. - Monien, Ramme, Salmen (1996)(Correct)
In this paper, we introduce a parallel simulated annealing algorithm
for generating aesthetically pleasing straight-line drawings. The
proposed algorithm calculates high quality 3D layouts of arbitr... / annealing SA is a flexible optimization method suited for large-scale br for large-scale combinatorial optimization problems DLS JAMS -
9 Approximating Maximum Clique with a Hopfield Network - Jagota (1995)(Correct)
In a graph, a clique is a set of vertices such that every pair is connected by an edge. MAX-CLIQUE is the optimization problem of finding the largest clique in a given graph, and is NP-hard, even to a... / by an edge. MAX-CLIQUE is the optimization problem of finding the largest br d c b a MAX-CLIQUE is the optimization problem of finding a largest
9 On Correlated Mutations in Evolution Strategies - Rudolph (1992)(Correct)
this paper we are interested mainly in convergence speed of ESs such that we shall assume for simplicity that f has only one local minimum which is of course the global one. First work on this topic h... / been developed for experimental optimization i.e. optimization at the real br experimental optimization i.e. optimization at the real object. Later they
9 A grid algorithm for bound constrained optimization of noisy functions - Elster, Neumaier (1995)(Correct)
The optimization of noisy functions is a common problem occurring in various applications. In this paper, a new approach is presented for low-dimensional bound constrained problems, based on the use... / algorithm for bound constrained optimization of noisy functions Clemens br ABSTRACT The optimization of noisy functions is a common
9 A Hardware Genetic Algorithm for the Traveling Salesman Problem on.. - Graham (1995)(Correct)
With the introduction of Splash, Splash 2, PAM, and other
reconfigurable computers, a wide variety of algorithms can now be feasibly
constructed in hardware. In this paper, we describe the Splash
2 ... / Holland developed an optimization technique based on the process br has shown its usefulness for optimization problems requiring the search
9 Criticality and Parallelism in Combinatorial Optimization - Macready, Siapas, Kauffman (1995)(Correct)
Local search methods constitute one of the most successful approaches to solving large-scale combinatorial optimization problems. A new result concerning the parallelization of such methods is present... / Parallelism in Combinatorial Optimization William G. Macready br large-scale combinatorial optimization problems. A new result
9 A Critique of Structure-from-Motion Algorithms - Oliensis (2000)(Correct)
I critique current approaches to structure from motion and describe a new framework for
algorithms.
Keywords Structure from motion, multi--frame structure from motion, projective methods,
invariants,... / Kalman filtering optimization trilinear reconstruction br approaches to SFM including optimization Kalman filtering and fusing
9 Nested Loop Sequences: Towards Efficient Loop Structures in Automatic .. - Chamski (1993)(Correct)
An important problem in automatic parallelization of scientific programs
is to generate loops from an algebraic description of the iteration domain.
The usual technique is to produce a perfectly nes... / linear programming optimization. R'esum'e tsvp Unit e br programmation lin'eaire optimisation. Nested Loop Sequences
9 Flow Visualization for Turbomachinery Design - Roth, Peikert (1996)(Correct)
Visualization of CFD data for turbomachinery design poses some special requirements which are often not addressed by standard flow visualization systems. We discuss the issues involved with this parti... / CFD is routinely used for optimization and comparison To br helicity with apparent vortices. Minima of pressure A technique which
9 Glopeq: A New Computational Tool For The Phase And Chemical.. - M.Mcdonald (1997)(Correct)
Calculation of phase and chemical equilibrium represents a crucial phase in the modeling
of many separation processes. For conditions of constant temperature and pressure, a
necessary and sufficient... / models GLOPEQ Global OPtimization for the Phase and chemical br phases. However the local optimization approaches which are in common
9 A Lagrangian Relaxation Network for Graph Matching - Rangarajan, Mjolsness (1996)(Correct)
A Lagrangian relaxation network for graph matching is presented. The problem is formulated as follows: given graphs G and g, find a permutation matrix M that brings the two sets of vertices into corre... / is converted into a nonlinear optimization problem with the match matrix br via and and subsequent optimization w.r.t. them is referred to as
9 Global Optimization For The Phase And Chemical Equilibrium Problem.. - Mcdonald (1994)(Correct)
Several approaches have been proposed for the computation of solutions to the phase
and chemical equilibrium problem when the problem is posed as the minimization of the
Gibbs free energy function. No... / Global Optimization For The Phase And Chemical br Secondly the Global OPtimization GOP algorithm Floudas and
9 An Analytical Constant Modulus Algorithm - van der Veen, Paulraj (1996)(Correct)
Iterative constant modulus algorithms such as Godard and
CMA have been used to blindly separate a superposition of co-channel constant
modulus (CM) signals impinging on an antenna array. These algorit... / of the gradient descent optimization the CMAs do converge sometimes br Additive Noise A. Equivalent Optimization Problem With Noise Added To
9 Optimal Structure From Motion: Local Ambiguities and Global Estimates - Soatto (1998)(Correct)
We present an analysis of SFM from the point of view of
noise. This analysis results in an algorithm that is provably
convergent and provably optimal with respect to a chosen norm.
In particular, we c... / we cast SFM as a nonlinear optimization problem and define a bilinear br generalpurpose nonlinear optimization techniques on cost functions
8 Unsupervised Learning by Convex and Conic Coding - Lee, Seung (1997)(Correct)
Unsupervised learning algorithms based on convex and conic encoders are proposed. The encoders find the closest convex or conic combination of basis vectors to the input. The learning algorithms produ... / of as a heavily constrained optimization of Eq. a single v a br with this constraint the optimization of Eq. becomes an integer
8 GSAT versus Simulated Annealing - Beringer, Aschemann, Hoos, Weiß (1994)(Correct)
The question of satisfiability for a given propositional
formula arises in many areas of AI. Especially finding
a model for a satisfiable formula is very important though
known to be NP-complete. Th... / which is often used for optimization and constraint satisfaction br C.D. Gelatt and M.P. Vecchi Optimization by simulated annealing'
8 GA-easy and GA-hard Constraint Satisfaction Problems - Eiben, Raué, Ruttkay (1995)(Correct)
In this paper we discuss the possibilities of applying genetic
algorithms (GA) for solving constraint satisfaction problems (CSP). We
point out how the greediness of deterministic classical CSP solvin... / done for discrete constrained optimisation problems br In systems for constrained optimization problems with continous
8 Global Optimization And Analysis For The Gibbs Free Energy Function.. - McDonald, Floudas (1994)(Correct)
The Wilson equation for the excess Gibbs energy has found wide use in successfully representing
the behavior of polar and nonpolar multicomponent mixtures with only binary
parameters, but was incapabl... / Global Optimization And Analysis For The Gibbs br is provided so that a local optimization technique will always converge
8 A novel optimizing network architecture with applications - Rangarajan, Gold, al. (1996)(Correct)
We present a novel optimizing network architecture with applications in vision, learning, pattern recognition and combinatorial optimization. This architecture is constructed by combining the followin... / recognition and combinatorial optimization. This architecture is br to many hard combinatorial optimization problems. The results for
8 A Multi-frame Structure-from-Motion Algorithm under Perspective.. - Oliensis (1997)(Correct)
We present a fast, robust algorithm for multi--frame structure from motion from point features which
works for general motion and large perspective effects. Experimental results on synthetic and real
... / the MATLAB implementation of optimization via Levenberg-Marquardt br We have tried several MATLAB optimization routines without obtaining
8 An Updated Survey of Evolutionary Multiobjective Optimization.. - Coello (1999)(Correct)
This paper reviews some of the most popular
evolutionary multiobjective optimization techniques currently
reported in the literature, indicating some of their
main applications, their advantages, disa... / of Evolutionary Multiobjective Optimization Techniques State of the Art br evolutionary multiobjective optimization techniques currently reported
8 A Stochastic Approach to the Weighted-Region Problem: Design and.. - Kindl, Shing, Rowe (1991)(Correct)
This paper presents an efficient heuristic algorithm for planning near-optimal high-level paths for
a point agent moving through complex terrain modeled by the Weighted-Region Problem. The input
to th... / search with probabilistic optimization by simulated annealing. It br it solves a convex local-optimization subproblem to find the
8 Effective Multifingered Grasp Synthesis - Jefferson Coelho Jr (1994)(Correct)
This report discusses the issues involved in developing a grasp controller, within the framework
of control composition, and introduces control pre-imaging. Pre-imaging is a design technique for
aug... / synthesis optimization-based grasp synthesis br solution contact geometry is an optimization based on shape rather than
8 Bayesian Inference on Visual Grammars by Neural Nets that Optimize - Mjolsness (1990)(Correct)
We exhibit a systematic way to derive neural nets for vision problems. It involves formulating a vision problem as Bayesian inference or decision on a comprehensive model of the visual domain given by... / distribution in terms of the optimization of an objective function E. br may be approximated by an optimization over near-permutations as we
8 A Family of Variable Metric Proximal Methods - Bonnans, Gilbert, Lemaréchal.. (1995)(Correct)
We consider conceptual optimization methods combining two ideas: the MoreauYosida
regularization in convex analysis, and quasi-Newton approximations of smooth functions.
We outline several approache... / We consider conceptual optimization methods combining two ideas br Bundle methods convex optimization global and superlinear
8 On-line learning processes in artificial neural networks - Heskes, Kappen (1993)(Correct)
We study on-line learning processes in artificial neural networks from a general point
of view. On-line learning means that a learning step takes place at each presentation of a
randomly drawn trainin... / of on-line learning for global optimization. In J. Taylor editor br might be used as a global optimization method. We derive cooling
8 An Iterative Improvement Algorithm for Low Power Data Path Synthesis - Raghunathan (1995)(Correct)
We address the problem of minimizing power consumption in behavioral synthesis of data-dominated circuits. The complex nature of power as a cost function implies that the effects of several behavioral... / methods of behavioral power optimization like data path replication and br work in architectural power optimization was presented in which
8 Molecular Modeling Of Proteins And Mathematical Prediction Of Protein .. - Neumaier (1997)(Correct)
This paper discusses the mathematical formulation of and solution attempts for the so-called protein folding problem. The static aspect is concerned with how to predict the folded (native, tertiary)... / data and the global optimization of the potential. The dynamic br conformational entropy global optimization simulated annealing genetic
8 A New Scheme for Incremental Learning - Jutten, Chentouf (1995)(Correct)
We present a new incremental procedure for supervised learning with noisy
data. Each step consists in adding to the current network a new unit which is
trained to learn the error of the network. The i... / ffl k x Because the optimization of the k-network and of the new br after a Least Square LS optimization is the realization of a
8 Using 3-Dimensional Meshes To Combine Image-Based and Geometry-Based.. - Fua, Leclerc (1994)(Correct)
A unified framework for 3--D shape reconstruction allows us to combine image-based and
geometry-based information sources. The image information is akin to stereo and shape-fromshading,
while the geom... / our weighting scheme and our optimization method. Finally we present br in an iterative manner using an optimization algorithm. Recent publications
8 Parallel Implementation of Algorithms for Finding Connected.. - Hsu, Ramachandran, Dean (1997)(Correct)
In this paper, we describe our implementation of several parallel graph algorithms for finding connected components. Our implementation, with virtual processing, is on a 16,384-processor MasPar MP-1... / Parallel processing of discrete optimization problems DIMACS series in br in trees and range minima More complex
8 An Intelligent, Predictive Control Approach to the High-Speed.. - Kelly (1995)(Correct)
Autonomous robot vehicles promise many ultimate civilian, military, and space applications. Off-road autonomous vehicles must engage the world exactly as they find it without relying on having it engi... / achieved through a quantitative optimization process. The reliability of br before obstacle avoidance. In optimization terms such a system considers
8 Cooperative - Competitive Genetic Evolution of Radial Basis Function.. - Whitehead, Choate (1995)(Correct)
In a radial basis function (RBF) network, the
RBF centers and widths can be evolved by a cooperativecompetitive
genetic algorithm. The set of genetic strings
in one generation of the algorithm represe... / approaches to neural network optimization have emphasized purely br various approaches to genetic optimization of neural networks fitting
7 Learning with preknowledge: clustering with point and graph matching.. - Gold, Rangarajan, al. (1996)(Correct)
Prior knowledge constraints are imposed upon a learning problem in the form of distance
measures. Prototypical 2-D point sets and graphs are learned by clustering with point matching
and graph matchin... / Learning is formulated as an optimization problem. Large objectives so br minimized using a combination of optimization techniques-softassign
7 Speeding up Variable Reordering of OBDDs - Meinel, Slobodova (1997)(Correct)
In this paper, we suggest a block-restricted sifting strategy which is based on the restriction of
Rudell's sifting to certain blocks of variables. The application of this strategy results in a consid... / sizes. The main resource for the optimization of the OBDD size is the choice br result in an essential loss in optimization while time could be decreased
7 Dynamic Hill Climbing: Overcoming the limitations of optimization.. - Yuret, Maza (1993)(Correct)
This paper describes a novel search algorithm, called dynamic hill climbing, that borrows ideas from genetic algorithms and hill climbing techniques. Unlike both genetic and hill climbing algorithms, ... / Overcoming the limitations of optimization techniques Deniz Yuret and br frame during the course of an optimization. Furthermore the algorithm
7 Combining Problem Reduction and Adaptive Multi-Start: A New Technique .. - Hagen (1995)(Correct)
VLSI netlist partitioning has been addressed chiefly by iterative methods (e.g. KernighanLin
[21] and Fiduccia-Mattheyses [13]) and spectral methods (e.g. Hagen-Kahng [14]). Iterative
methods are the ... / points for the iterative optimization based on the results of br based on the results of previous optimizations. The resulting Clustered
7 Efficient Massively Parallel Implementation of Some Combinatorial.. - Hsu, Ramachandran (1996)(Correct)
We describe our implementation of several efficient parallel algorithms on the massively parallel SIMD machine MasPar MP-1 with virtual processing. The MPL language that we used on the MasPar MP-1 doe... / of parallel primitives and optimization techniques used and presents br Parallel processing of discrete optimization problems volume of DIMACS
7 An Automatic Method Of Finding Topic Boundaries - Reynar (1994)(Correct)
This article outlines a new method of locating discourse
boundaries based on lexical cohesion and a graphical
technique called dotplotting. The application of dotplotting
to discourse segmentation can... / or automatically using an optimization algorithm. The results of two br correspond to the most extreme minima-those at positions
7 The Exit Path Of A Markov Chain With Rare Transitions - Catoni, Cerf (1995)(Correct)
We study the exit path from a general domain after the last visit to a set of a Markov chain with rare transitions. We prove several large deviation principles for the law of the succession of the c... / from statistical mechanics to optimization and reliability theory. This br state to a stable one. For an optimization algorithm it is described as
7 Self-Organizing Maps: Generalizations and New Optimization Techniques - Graepel, Burger, Obermayer (1998)(Correct)
We offer three algorithms for the generation of topographic mappings to the practitioner of unsupervised data analysis. The algorithms are each based on the minimization of a cost function which is pe... / Maps Generalizations and New Optimization Techniques Thore Graepel br maps of Euclidean data. Its optimization scheme however offers an
7 Automated Decomposition of Model-based Learning Problems - Brian Williams (1996)(Correct)
A new generation of sensor rich, massively distributed
autonomous systems is being developed that has
the potential for unprecedented performance, such
as smart buildings, reconfigurable factories, ad... / a -dimensional nonlinear optimization problem involving a br the concept of a conflict a minimal subset of a model typically in
7 Complexity Classes of Optimization Functions - Vollmer, Wagner (1995)(Correct)
In this paper, complexity classes of functions defined via taking maxima or minima
(cf. the work of Krentel) or taking middle elements (cf. the work of Toda) are examined.
A number of axioms for a cla... / Complexity Classes of Optimization Functions Heribert br a so called p-founded class of optimization functions are given. It is
7 Text Categorization Using Weight Adjusted k-Nearest Neighbor.. - Han, Karypis, Kumar (1999)(Correct)
Text categorization is the task of deciding whether a document belongs to a set of prespecified classes of documents.
Automatic classification schemes can greatly facilitate the process of categorizat... / weight using conjugate gradient optimization She Unlike PEBLS VSM br each iteration according to an optimization function. This algorithm is
7 Globally Optimal Regions and Boundaries - Jermyn, Ishikawa (1999)(Correct)
We propose a new form of energy functional for the segmentation
of regions in images, and an efficient method for
finding its global optima. The energy can have contributions
from both the region and ... / footing and solve the global optimization problem as a minimum mean br the region. The solution to the optimization problem is the global maximum
7 Approximation algorithms for MAX 4-SAT and rounding procedures for.. - Halperin, Zwick (1999)(Correct)
Karloff and Zwick obtained recently an optimal 7=8-approximation
algorithm for MAX 3-SAT. In an attempt to see whether similar
methods can be used to obtain a 7=8-approximation algorithm for MAX
... / SAT is one of the most natural optimization problems. An instance of MAX br goal. The fact that numerical optimization techniques were used to compute
7 Genetic Algorithms for Gait Synthesis in a Hexapod Robot - Lewis, Fagg, Bekey (1994)(Correct)
This paper describes the staged evolution of a complex motor pattern generator
(CPG) for the control of the leg movements of a six-legged walking robot. The
CPG is composed of a network of neurons. ... / gaits of animals and proposed optimization criteria for gaits and compares br such a function displays multiple minima in the parameter space and most
7 Error Bounds for Analytic Systems and Their Applications - Luo (1994)(Correct)
Using a 1958 result of Lojasiewicz, we establish an error bound for analytic systems consisting of equalities and inequalities defined by real analytic functions. In particular, we show that over any ... / in SIAM Journal on Control and Optimization. O. Guler A.J. Hoffman br SIAM Journal on Control and Optimization - .
7 Neural Networks and Complexity Theory - Orponen (1992)(Correct)
We survey some of the central results in the complexity theory
of discrete neural networks, with pointers to the literature.
1 Introduction
The recently revived field of computation by "neural" n... / and solving combinatorial optimization problems. Numerous algorithms br in finding the absolutely minimal circuit responding correctly to a
7 Proper Orthogonal Decomposition for Flow Calculations and Optimal.. - Ly, Tran (1998)(Correct)
Proper orthogonal decomposition (which is also known as the Karhunen
Lo`eve decomposition) is a reduction method that is used to obtain low
dimensional dynamic models of distributed parameter system... / In Ito et al. a shape optimization problem with respect to the br conditions and a numerical optimization method based on the augmented
7 The Racing Algorithm: Model Selection for Lazy Learners - Maron, Moore (1997)(Correct)
Given a set of models and some training data, we would like to find the model
that best describes the data. Finding the model with the lowest generalization error
is a computationally expensive proces... / the number of models is large. Optimization techniques such as hill br Genetic Algorithms in Search Optimization and Machine Learning.
6 Memetic Algorithms and the Fitness Landscape of the Graph.. - Merz, Freisleben (1998)(Correct)
In this paper, two types of fitness landscapes of the graph bipartitioning
problem are analyzed, and a memetic algorithm -- a genetic
algorithm incorporating local search -- that finds near-optimum ... / is an NP-hard combinatorial optimization problem that arises in br b general-purpose heuristic optimization approaches such as simulated
6 The Average Case Analysis Of Algorithms - Saddle Point Asymptotics - Flajolet, Sedgewick (1994)(Correct)
This report is part of a series whose aim is to present in a synthetic way
the major methods of "analytic combinatorics" needed in the average--case
analysis of algorithms. The series should comprise ... / shortly. Notice that the optimization problem need not be solved br are useful in combinatorial optimization the knapsack problem and
6 On Training Neural Nets through Stochastic Minimization - Brunelli (1994)(Correct)
The revival of multilayer neural networks in
the mid 80's originated from the discovery of the backpropagation
technique as a feasible training procedure.
In spite of its shortcomings, it is probably ... / Keywords stochastic optimization learning algorithms br represent two alternative optimization techniques based on random
6 Multidimensional Scaling and Data Clustering - Hofmann, Buhmann (1995)(Correct)
Visualizing and structuring pairwise dissimilarity data are difficult combinatorial optimization
problems known as multidimensional scaling or pairwise data clustering.
Algorithms for embedding dissi... / data are difficult combinatorial optimization problems known as br Euclidian space is a non-convex optimization problem which typically
6 Lagrangian Techniques for Solving a Class of Zero-One Integer Linear.. - Chang (1995)(Correct)
We consider a class of zero-one integer programming feasibility problems (0-1 ILPF problems) in which the coefficients of variables can be integers, and the objective is to find an assignment of binar... / into an unconstrained global optimization problem in real space. Using a br methods for solving constrained optimization problems. Little work has been
6 How good are genetic algorithms at finding large cliques: an.. - Carter (1993)(Correct)
This paper investigates the power of genetic algorithms at solving the MAX-CLIQUE problem. We measure the performance of a standard genetic algorithm on an elementary set of problem instances consisti... / Challenge on Combinatorial Optimization DIMACS October . y br are general-purpose optimization methods that have gained wide
6 Recognizing Emotion In Speech - Dellaert, Polzin, Waibel (1996)(Correct)
This paper explores several statistical pattern recognition
techniques to classify utterances according to their emotional
content. We have recorded a corpus containing emotional speech
with over a 10... / Set B. . Distance Metric Optimization In This And The Following br is quite rugged so classical optimization techniques like gradient
6 Guided Local Search - Voudouris, Tsang (1995)(Correct)
Guided Local Search (GLS) is an intelligent search scheme for combinatorial optimization
problems. A main feature of the approach is the iterative use of local search. Information is
gathered from var... / search scheme for combinatorial optimization problems. A main feature of br with well-known and established optimization techniques such as simulated
6 Multidimensional Scaling by Deterministic Annealing - Klock, Buhmann (1997)(Correct)
Multidimensional scaling addresses the problem how proximity
data can be faithfully visualized as points in a low-dimensional
Euclidian space. The quality of a data embedding is measured by a cost
f... / coordinates for this continuous optimization problem. Experimental results br the superiority of the optimization technique compared to
6 A Global Optimization Approach for Lennard-Jones Microclusters - Maranas, Floudas (1992)(Correct)
A global optimization approach is proposed for finding the global minimum energy configuration of Lennard--Jones microclusters. First, the original nonconvex total potential energy function, composed ... / A Global Optimization Approach for Lennard-Jones br Abstract A global optimization approach is proposed for
6 A Study of the Mean Field Approach to Knapsack Problems - Ohlsson, Pi (1997)(Correct)
The mean field theory (MFT) approach to knapsack problems is extended to multiple knapsacks and generalized assignment problems with Potts mean field equations governing the dynamics. Numerical test... / example of a combinatorial optimization problem with inequality br to difficult combinatorial optimization problems The
6 SDO: A Statistical Method for Global Optimization - Cox, John (1997)(Correct)
An algorithm for finding global optima using statistical prediction is presented. Assuming a random function model, lower confidence bounds on predicted values are used for sequential selection of eva... / A Statistical Method for Global Optimization Dennis D. Cox y br in the problem of global optimization of nonlinear functions since
6 Parallel Approaches to Stochastic Global Optimization - Rudolph (1992)(Correct)
In this paper we review parallel implementations of some stochastic
global optimization methods on MIMD computers. Moreover, we
present a new parallel version of an Evolutionary Algorithm for global
o... / Approaches to Stochastic Global Optimization Gunter Rudolph br of some stochastic global optimization methods on MIMD computers.
6 Handling Inequality Constraints In Continuous Nonlinear Global.. - Tao Wang (1996)(Correct)
In this paper, we present a new method to handle inequality
constraints and apply it in NOVEL (Nonlinear
Optimization via External Lead), a system we have developed
for solving constrained continuous ... / In Continuous Nonlinear Global Optimization Tao Wang And Benjamin W. br apply it in NOVEL Nonlinear Optimization via External Lead a system we
6 The Theory of Discrete Lagrange Multipliers for Nonlinear Discrete.. - Wah, Wu (1999)(Correct)
In this paper we present a Lagrange-multiplier formulation of
discrete constrained optimization problems, the associated discrete-space
first-order necessary and sufficient conditions for saddle poi... / for Nonlinear Discrete Optimization Benjamin W. Wah and Zhe br of discrete constrained optimization problems the associated