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3,768
The Extended Linear Complementarity Problem
, 1993
"... We consider an extension of the horizontal linear complementarity problem, which we call the extended linear complementarity problem (XLCP). With the aid of a natural bilinear program, we establish various properties of this extended complementarity problem; these include the convexity of the biline ..."
Abstract

Cited by 788 (30 self)
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of the bilinear objective function under a monotonicity assumption, the polyhedrality of the solution set of a monotone XLCP, and an error bound result for a nondegenerate XLCP. We also present a finite, sequential linear programming algorithm for solving the nonmonotone XLCP.
Mining Sequential Patterns
, 1995
"... We are given a large database of customer transactions, where each transaction consists of customerid, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empiri ..."
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Cited by 1568 (6 self)
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We are given a large database of customer transactions, where each transaction consists of customerid, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem
An Adaptive Sequential Linear Programming Algorithm for
 Optimal Design Problems With Probabilistic Constraints,” ASME
, 2007
"... Optimal design problems with probabilistic constraints, often referred to as reliabilitybased design optimization problems, have been the subject of extensive recent studies. Solution methods to date have focused more on improving efficiency rather than accuracy and the global convergence behavior o ..."
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Cited by 13 (8 self)
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of the solution. A new strategy utilizing an adaptive sequential linear programming (SLP) algorithm is proposed as a promising approach to balance accuracy, efficiency, and convergence. The strategy transforms the nonlinear probabilistic constraints into equivalent deterministic ones using both first order
Mining Sequential Patterns: Generalizations and Performance Improvements
 RESEARCH REPORT RJ 9994, IBM ALMADEN RESEARCH
, 1995
"... The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transactiontime, and each transaction is a set of items. The problem is to discover all sequential patterns with a userspecified ..."
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Cited by 759 (5 self)
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these generalized sequential patterns. Empirical evaluation using synthetic and reallife data indicates that GSP is much faster than the AprioriAll algorithm presented in [3]. GSP scales linearly with the number of datasequences, and has very good scaleup properties with respect to the average datasequence size.
Sequential linear interpolation of multidimensional functions
 IEEE TRANSACTIONS ON IMAGE PROCESSING
, 1997
"... ..."
Sequential minimal optimization: A fast algorithm for training support vector machines
 Advances in Kernel MethodsSupport Vector Learning
, 1999
"... This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possi ..."
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Cited by 461 (3 self)
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This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest
Building Design Optimization Using Sequential Linear Programming
"... AbstractIn this paper a nonlinear programming approach is used for the minimization of total communication cost to determine the optimum room dimensions for each room. The nonlinear programming problem is solved by the Improved Move Limit Method of Sequential Linear Programming. In this method, t ..."
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Cited by 2 (0 self)
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AbstractIn this paper a nonlinear programming approach is used for the minimization of total communication cost to determine the optimum room dimensions for each room. The nonlinear programming problem is solved by the Improved Move Limit Method of Sequential Linear Programming. In this method
SEQUENTIALLY LINEAR SAWTOOTH MODELLING OF REINFORCED STRUCTURES
"... A sequentially linear sawtooth continuum model which captures the nonlinear response via a series of linear steps is presented. In the model, the softening stressstrain curve with negative slope is replaced by a sawtooth diagram of positive slopes, while the incrementaliterative procedure is rep ..."
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A sequentially linear sawtooth continuum model which captures the nonlinear response via a series of linear steps is presented. In the model, the softening stressstrain curve with negative slope is replaced by a sawtooth diagram of positive slopes, while the incrementaliterative procedure
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 597 (24 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have bee ..."
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Cited by 770 (3 self)
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Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have
Results 1  10
of
3,768