LOQO用户手册-版本3.10

IF 1.4 3区数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Optimization Methods & Software Pub Date : 1999-01-01 DOI:10.1080/10556789908805760

R. Vanderbei

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引用次数: 130

摘要

LOQO是一种求解光滑约束优化问题的系统。问题可以是线性的或非线性的，凸的或非凸的，有约束的或无约束的。唯一真正的限制是定义问题的函数是平滑的(在算法计算的点上)。如果问题是凸的，LOQO会找到一个全局最优解。否则，它会在给定起始点附近找到一个局部最优解。本手册描述如下:1。如何在硬件上安装LOQO。2. 2 .如何将AMPL与LOQO结合起来解决一般的优化问题;如何使用子程序库来制定和求解优化问题;如何在MPS格式中制定和求解线性和二次规划。

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LOQO user's manual — version 3.10

LOQO is a system for solving smooth constrained optimization problems. The problems can be linear or nonlinear, convex or nonconvex, constrained or unconstrained. The only real restriction is that the functions defining the problem be smooth (at the points evaluated by the algorithm). If the problem is convex, LOQO finds a globally optimal solution. Otherwise, it finds a locally optimal solution near to a given starting point. This manual describes 1. how to install LOQO on your hardware. 2. how to use AMPL together with LOQO to solve general optimization problems, 3. how to use the subroutine library to formulate and solve optimization problems, and 4. how to formulate and solve linear and quadratic programs in MPS format.

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来源期刊

Optimization Methods & Software 工程技术-计算机：软件工程

CiteScore

4.50

自引率

0.00%

发文量

审稿时长

7 months

期刊介绍： Optimization Methods and Software publishes refereed papers on the latest developments in the theory and realization of optimization methods, with particular emphasis on the interface between software development and algorithm design. Topics include: Theory, implementation and performance evaluation of algorithms and computer codes for linear, nonlinear, discrete, stochastic optimization and optimal control. This includes in particular conic, semi-definite, mixed integer, network, non-smooth, multi-objective and global optimization by deterministic or nondeterministic algorithms. Algorithms and software for complementarity, variational inequalities and equilibrium problems, and also for solving inverse problems, systems of nonlinear equations and the numerical study of parameter dependent operators. Various aspects of efficient and user-friendly implementations: e.g. automatic differentiation, massively parallel optimization, distributed computing, on-line algorithms, error sensitivity and validity analysis, problem scaling, stopping criteria and symbolic numeric interfaces. Theoretical studies with clear potential for applications and successful applications of specially adapted optimization methods and software to fields like engineering, machine learning, data mining, economics, finance, biology, or medicine. These submissions should not consist solely of the straightforward use of standard optimization techniques.