Results 1  10
of
26
VLSI cell placement techniques
 ACM Computing Surveys
, 1991
"... VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasi ..."
Abstract

Cited by 75 (0 self)
 Add to MetaCart
VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasis on standard ce11and macro
Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
 SIAM Journal on Optimization
, 2004
"... A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for gene ..."
Abstract

Cited by 37 (8 self)
 Add to MetaCart
A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results are presented that reduce seamlessly to existing results for more specific classes of problems. While no local continuity or smoothness assumptions are required to apply the algorithm, a hierarchy of theoretical convergence results based on the Clarke calculus is given, in which local smoothness dictate what can be proved about certain limit points generated by the algorithm. To demonstrate the usefulness of the algorithm, the algorithm is applied to the design of a loadbearing thermal insulation system. We believe this is the first algorithm with provable convergence results to directly target this class of problems.
Simulated Annealing with Extended Neighbourhood
, 1991
"... Simulated Annealing (SA) is a powerful stochastic search method applicable to a wide range of problems for which little prior knowledge is available. It can produce very high quality solutions for hard combinatorial optimization problems. However, the computation time required by SA is very large. V ..."
Abstract

Cited by 21 (14 self)
 Add to MetaCart
Simulated Annealing (SA) is a powerful stochastic search method applicable to a wide range of problems for which little prior knowledge is available. It can produce very high quality solutions for hard combinatorial optimization problems. However, the computation time required by SA is very large. Various methods have been proposed to reduce the computation time, but they mainly deal with the careful tuning of SA's control parameters. This paper first analyzes the impact of SA's neighbourhood on SA's performance and shows that SA with a larger neighbourhood is better than SA with a smaller one. The paper also gives a general model of SA, which has both dynamic generation probability and acceptance probability, and proves its convergence. All variants of SA can be unified under such a generalization. Finally, a method of extending SA's neighbourhood is proposed, which uses a discrete approximation to some continuous probability function as the generation function in SA, and several impo...
Characterization Of Signals By The Ridges Of Their Wavelet Transforms
 IEEE Trans. on Signal Processing
, 1994
"... We present a couple of new algorithmic procedures for the detection of ridges in the modulus of the (continuous) wavelet transform of onedimensional signals. These detection procedures are shown to be robust to additive white noise. We also derive and test a new reconstruction procedure. The latter ..."
Abstract

Cited by 18 (4 self)
 Add to MetaCart
We present a couple of new algorithmic procedures for the detection of ridges in the modulus of the (continuous) wavelet transform of onedimensional signals. These detection procedures are shown to be robust to additive white noise. We also derive and test a new reconstruction procedure. The latter uses only information from the restriction of the wavelet transform to a sample of points from the ridge. This provides with a very efficient way to code the information contained in the signal. Partially supported by ONR N00014911010 y Supported by NSF IBN 9405146 1 Introduction The characterization and the separation of amplitude and frequency modulated signals is a classical problem of signal analysis and signal processing. Applications can be found in many situations, such as for instance radar/sonar detection and speech processing [9]. Many methods have been proposed in the past few years to analyze the timefrequency localization of signals. The most noticeable are the family...
Global Optimization For Constrained Nonlinear Programming
, 2001
"... In this thesis, we develop constrained simulated annealing (CSA), a global optimization algorithm that asymptotically converges to constrained global minima (CGM dn ) with probability one, for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary ..."
Abstract

Cited by 12 (2 self)
 Add to MetaCart
In this thesis, we develop constrained simulated annealing (CSA), a global optimization algorithm that asymptotically converges to constrained global minima (CGM dn ) with probability one, for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary and sufficient condition for constrained local minima (CLM dn ) in the theory of discrete constrained optimization using Lagrange multipliers developed in our group. The theory proves the equivalence between the set of discrete saddle points and the set of CLM dn, leading to the firstorder necessary and sufficient condition for CLM dn. To find
MultiRidge Detection and TimeFrequency Reconstruction
 IEEE Transactions on Signal Processing
, 1996
"... The ridges of the wavelet transform, the Gabor transform or any timefrequency representation of a signal contain crucial information on the characteristics of the signal. Indeed they mark the regions of the timefrequency plane where the signal concentrates most of its energy. We introduce a new ..."
Abstract

Cited by 11 (4 self)
 Add to MetaCart
The ridges of the wavelet transform, the Gabor transform or any timefrequency representation of a signal contain crucial information on the characteristics of the signal. Indeed they mark the regions of the timefrequency plane where the signal concentrates most of its energy. We introduce a new algorithm to detect and identify these ridges. The procedure is based on an original penalization of the transitions of the random walk in a bounded domain of the plane. We show that this detection algorithm is especially useful for noisy signals with multiridge transforms. It is a common practice among practitioners to reconstruct a signal from the skeleton of a transform of the signal (i.e. the restriction of the transform to the ridges). After reviewing several known procedures we introduce a new reconstruction algorithm and we illustrate its usefulness on speech signals. Partially supported by ONR N00014911010 y Supported by NSF IBN 9405146 1 1 Introduction and Notations ...
Tuning Strategies In Constrained Simulated Annealing For Nonlinear Global Optimization
 Int’l J. of Artificial Intelligence Tools
, 2000
"... This paper studies various strategies in constrained simulated annealing (CSA), a global optimization algorithm that achieves asymptotic convergence to constrained global minima (CGM) with probability one for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
This paper studies various strategies in constrained simulated annealing (CSA), a global optimization algorithm that achieves asymptotic convergence to constrained global minima (CGM) with probability one for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary and sufficient condition for discrete constrained local minima (CLM) in the theory of discrete Lagrange multipliers and its extensions to continuous and mixedinteger constrained NLPs. The strategies studied include adaptive neighborhoods, distributions to control sampling, acceptance probabilities, and cooling schedules. We report much better solutions than the bestknown solutions in the literature on two sets of continuous benchmarks and their discretized versions.
Optimal Anytime Search For Constrained Nonlinear Programming
, 2001
"... In this thesis, we study optimal anytime stochastic search algorithms (SSAs) for solving general constrained nonlinear programming problems (NLPs) in discrete, continuous and mixedinteger space. The algorithms are general in the sense that they do not assume di#erentiability or convexity of functio ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
In this thesis, we study optimal anytime stochastic search algorithms (SSAs) for solving general constrained nonlinear programming problems (NLPs) in discrete, continuous and mixedinteger space. The algorithms are general in the sense that they do not assume di#erentiability or convexity of functions. Based on the search algorithms, we develop the theory of SSAs and propose optimal SSAs with iterative deepening in order to minimize their expected search time. Based on the optimal SSAs, we then develop optimal anytime SSAs that generate improved solutions as more search time is allowed. Our SSAs
Stochastic Simulation On Integer Constraint Sets
 SIAM J. Optimization
, 1998
"... . Bounds are given on the number of steps su#cient for convergence of simulation algorithms on domains of nonnegative integer constraint sets. Key words. Markov chains, eigenvalues, annealing, integer optimization AMS subject classifications.
Abstract

Cited by 6 (0 self)
 Add to MetaCart
.<F3.883e+05> Bounds are given on the number of steps su#cient for convergence of simulation algorithms on domains of nonnegative integer constraint sets.<F4.005e+05> Key words.<F3.883e+05> Markov chains, eigenvalues, annealing, integer optimization<F4.005e+05> AMS subject classifications.<F3.883e+05> 60J20, 65K05, 90C10<F4.005e+05> PII.<F3.883e+05> S1052623496313842<F4.721e+05> 1. Introduction.<F4.501e+05> This article is concerned with convergence of Markov chains on nonnegative integer constraint sets and applications to simulated annealing algorithms for optimization. Despite the lack of applicable results on its performance, the annealing algorithm is used for optimization of nonlinear functions on discrete domains. One application of the algorithm is finding modes of probability distributions on finite sets, a problem which arises in Bayesian statistics and image analysis (see [6] and [14]). It is used for other problems in combinatorial optimization as well, some of which are de...
The Theory And Applications Of Discrete Constrained Optimization Using Lagrange Multipliers
, 2000
"... In this thesis, we present a new theory of discrete constrained optimization using Lagrange multipliers and an associated firstorder search procedure (DLM) to solve general constrained optimization problems in discrete, continuous and mixedinteger space. The constrained problems are general in the ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
In this thesis, we present a new theory of discrete constrained optimization using Lagrange multipliers and an associated firstorder search procedure (DLM) to solve general constrained optimization problems in discrete, continuous and mixedinteger space. The constrained problems are general in the sense that they do not assume the differentiability or convexity of functions. Our proposed theory and methods are targeted at discrete problems and can be extended to continuous and mixedinteger problems by coding continuous variables using a floatingpoint representation (discretization). We have characterized the errors incurred due to such discretization and have proved that there exists upper bounds on the errors. Hence, continuous and mixedinteger constrained problems, as well as discrete ones, can be handled by DLM in a unified way with bounded errors.