This paper describes a technique for generating disjointly constrained bilinear programming test problems with known solutions and properties. The proposed construction technique applies a simple random transformation...
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This paper describes a technique for generating disjointly constrained bilinear programming test problems with known solutions and properties. The proposed construction technique applies a simple random transformation of variables to a separable bilinear programming problem that is constructed by combining disjoint low-dimensional bilinear programs.
Using only well-known theorems of the differential calculus, we derive necessary conditions for a relative minimum of a nonlinear differentiable objective function of nonnegative variables constrained by nonlinear dif...
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Using only well-known theorems of the differential calculus, we derive necessary conditions for a relative minimum of a nonlinear differentiable objective function of nonnegative variables constrained by nonlinear differentiable inequalities. The results are expressed entirely in terms of partial derivatives, which are subsequently identified with the Lagrange multipliers of the Kuhn-Tucker nonlinear programming theorem. Our conditions may be considered therefore as the Kuhn-Tucker theorem in differential rather than Lagrangian form.
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ISBN:
(数字)9781611978308
ISBN:
(纸本)9781611978292
Get 10% off by pre-ordering this book.
This item has not yet published. Pre-order now and we will ship and process payment when the book becomes available.
This self-contained textbook provides the foundations of linear optimization, covering topics in both continuous and discrete linear optimization. It gradually builds the connection between theory, algorithms, and applications so that readers gain a theoretical and algorithmic foundation, familiarity with a variety of applications, and the ability to apply the theory and algorithms to actual problems.
To deepen the reader's understanding, the authors provide
many applications from diverse areas of applied sciences, such as resource allocation, line fitting, graph coloring, the traveling salesman problem, game theory, and network flows;
more than 180 exercises, most of them with partial answers and about 70 with complete solutions; and
a continuous illustration of the theory through examples and exercises.
A First Course in linear Optimization is intended to be read cover to cover and requires only a first course in linear algebra as a prerequisite. Its 13 chapters can be used as lecture notes for a first course in linear optimization.
Transmission congestion is a major barrier between supplying cheap generation to load centers. Unified Power Flow Controller(UPFC) can help relive transmission congestion. For integrating a UPFC within the optimal pow...
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Integer programming (IP) is an extension of linear programming (LP) whereby the goal is to determine values for a set of decision variables (some or all of which have integer restrictions) so as to maximize or minimiz...
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The “cutting plane” method of Kelley for nonlinear programming problems applies linear programming, through a sequence of local linearizations, to the problem of minimizing a convex function of real variables subjec...
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The “cutting plane” method of Kelley for nonlinear programming problems applies linear programming, through a sequence of local linearizations, to the problem of minimizing a convex function of real variables subject to linear inequality constraints. A procedure is presented here for improving the constructed linearizations which may considerably accelerate the convergence of the process. In the case of a quadratic objective function satisfying certain mild conditions this improvement yields a finite algorithm.
There is a strong need for the fast solution of large and dense linear systems which arise from the interior-point method for solving Semidefinite programming with its dual problem. Often direct methods are too expens...
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There is a strong need for the fast solution of large and dense linear systems which arise from the interior-point method for solving Semidefinite programming with its dual problem. Often direct methods are too expensive in terms of computer memory and CPU-time requirements, then the only alternative is to use iterative methods. Here, a class of incomplete orthogonalization preconditioners for the conjugate gradient method for solving this type of linear systems will be proposed. The efficient feature of the preconditioners will be confirmed by several numerical experiments. (C) 2001 IMACS. Published by Elsevier Science B.V. All rights reserved.
We introduce a constraint system LC that handles arithmetic constraints over reals within the linear concurrent constraint programming (Icc) framework. This approach provides us with a general, extensible foundation f...
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ISBN:
(纸本)3540652248
We introduce a constraint system LC that handles arithmetic constraints over reals within the linear concurrent constraint programming (Icc) framework. This approach provides us with a general, extensible foundation for linear programming algorithm design that comes with a (linear) logical semantics. In particular, it allows us to build a 'glass-box' version of the (constraint solver) simplex algorithm by defining (monotone) cc ask and tell agents over a higher-level constraint system as Icc(LC) programs. We illustrate at the same time the use of the lccframework as a non-trivial concurrent algorithm specification tool.
SMT solvers combine SAT reasoning with specialized theory solvers either to find a feasible solution to a set of constraints or to prove that no such solution exists. linear programming (LP) solvers come from the trad...
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ISBN:
(纸本)9780983567844
SMT solvers combine SAT reasoning with specialized theory solvers either to find a feasible solution to a set of constraints or to prove that no such solution exists. linear programming (LP) solvers come from the tradition of optimization, and are designed to find feasible solutions that are optimal with respect to some optimization function. Typical LP solvers are designed to solve large systems quickly using floating point arithmetic. Because floating point arithmetic is inexact, rounding errors can lead to incorrect results, making inexact solvers inappropriate for direct use in theorem proving. Previous efforts to leverage such solvers in the context of SMT have concluded that in addition to being potentially unsound, such solvers are too heavyweight to compete in the context of SMT. In this paper, we describe a technique for integrating LP solvers that improves the performance of SMT solvers without compromising correctness. These techniques have been implemented using the SMT solver CVC4 and the LP solver GLPK. Experiments show that this implementation outperforms other state-of-the-art SMT solvers on the QF LRA SMT-LIB benchmarks and is competitive on the QF LIA benchmarks.
We present a system-level model with an on-chip temperature compensation technique for a CMOS-MEMS monolithic calorimetric flow sensing *** model encompasses mechanical,thermal,and electrical domains to facilitate the...
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We present a system-level model with an on-chip temperature compensation technique for a CMOS-MEMS monolithic calorimetric flow sensing *** model encompasses mechanical,thermal,and electrical domains to facilitate the co-design of a MEMS sensor and CMOS interface circuits on the EDA *** compensation strategy is implemented on-chip with a variable temperature difference heating *** show that the linear programming for the low-temperature drift in the SoC output is characterized by a compensation resistor Rc with a resistance value of 748.21Ωand a temperature coefficient of resistance of 3.037×10−3℃^(−1) at 25℃.Experimental validation demonstrates that within an ambient temperature range of 0–50℃ and a flow range of 0-10 m/s,the temperature drift of the sensor is reduced to±1.6%,as compared to±8.9%observed in a counterpart with the constant temperature difference ***,this on-chip temperature-compensated CMOS-MEMS flow sensing SoC is promising for low-cost sensing applications such as respiratory monitoring and smart energy-efficient buildings.
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