BRANCH AND BOUND WITH SIMPLICIAL PARTITIONS FOR GLOBAL OPTIMIZATION

Branch and bound methods for global optimization are considered in this paper. Advantages and disadvantages of simplicial partitions for branch and bound are shown. A new general combinatorial approach for vertex triangulation of hyper‐rectangular feasible regions is presented. Simplicial partitions may be used to vertex triangulate feasible regions of non rectangular shape defined by linear inequality constraints. Linear inequality constraints may be used to avoid symmetries in optimization problems.

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A class of derivative-free trust-region methods with interior backtracking technique for nonlinear optimization problems subject to linear inequality constraints

Journal of Inequalities and Applications ◽

10.1186/s13660-018-1698-7 ◽

2018 ◽

Vol 2018 (1) ◽

Cited By ~ 2

Author(s):

Jing Gao ◽

Jian Cao

Keyword(s):

Nonlinear Optimization ◽

Linear Inequality ◽

Optimization Problems ◽

Trust Region ◽

Inequality Constraints ◽

Trust Region Methods ◽

Linear Inequality Constraints ◽

Derivative Free

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Geometric branch-and-bound methods for constrained global optimization problems

Journal of Global Optimization ◽

10.1007/s10898-012-9961-9 ◽

2012 ◽

Vol 57 (3) ◽

pp. 771-782 ◽

Cited By ~ 1

Author(s):

Daniel Scholz

Keyword(s):

Global Optimization ◽

Branch And Bound ◽

Optimization Problems ◽

Constrained Global Optimization ◽

Branch And Bound Methods

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A new test for linear inequality constraints when the variance–covariance matrix depends on the unknown parameters

Economics Letters ◽

10.1016/j.econlet.2011.07.018 ◽

2011 ◽

Vol 113 (3) ◽

pp. 241-243 ◽

Cited By ~ 5

Author(s):

Stephen G. Donald ◽

Yu-Chin Hsu

Keyword(s):

Covariance Matrix ◽

Linear Inequality ◽

Inequality Constraints ◽

Unknown Parameters ◽

Linear Inequality Constraints ◽

Variance Covariance Matrix

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First-order and second-order ?-directional derivatives of a marginal function in convex programming with linear inequality constraints

Journal of Optimization Theory and Applications ◽

10.1007/bf00940934 ◽

1990 ◽

Vol 66 (3) ◽

pp. 489-502 ◽

Cited By ~ 2

Author(s):

S. Shiraishi

Keyword(s):

Convex Programming ◽

Linear Inequality ◽

Inequality Constraints ◽

Second Order ◽

Directional Derivatives ◽

Marginal Function ◽

First Order ◽

Linear Inequality Constraints ◽

Derivatives Of

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A new approach to the analysis of random methods for detecting necessary linear inequality constraints

Mathematical Programming ◽

10.1007/bf01582281 ◽

1989 ◽

Vol 43 (1-3) ◽

pp. 97-102 ◽

Cited By ~ 7

Author(s):

Richard J. Caron ◽

J. F. McDonald

Keyword(s):

Linear Inequality ◽

Inequality Constraints ◽

New Approach ◽

Linear Inequality Constraints ◽

Random Methods

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Mathematical model for determining rational machining conditions for flat grinding with the wheel periphery on machines with a rectangular table

MATEC Web of Conferences ◽

10.1051/matecconf/201822401112 ◽

2018 ◽

Vol 224 ◽

pp. 01112

Author(s):

Dmitriy L. Skuratov ◽

Dmitriy G. Fedorov ◽

Dmitriy V. Evdokimov

Keyword(s):

Mathematical Model ◽

Objective Function ◽

Linear Inequality ◽

Inequality Constraints ◽

Machining Time ◽

Functional Parameters ◽

Linear Inequality Constraints ◽

Machining Quality ◽

Machining Conditions ◽

Grinding Operations

A mathematical model is presented for determining the rational machining conditions for flat grinding operations by the rim of a wheel on machines with a rectangular table consisting of a linear objective function and linear inequality constraints. As the objective function, the equation, determining the main machining time, was used. And constraints which are related to the functional parameters and parameters determining the machining quality and the kinematic capabilities of the machine were used as inequality constraints.

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