### Abstract

disjoint linear constraints is presented. In the considered class of

problems, a subset of variables are subject to linear equality constraints,

while variables in a different subset are constrained to remain in a convex set.

The proposed algorithm exploits the structure by combining steps in the

nullspace of the equality constraint's matrix with projections onto the convex set. The algorithm is motivated by application in weather forecasting.

Numerical results on a simple model designed for predicting rain show that the

algorithm is an improvement on current practice and that it reduces the

computational burden compared to a more general interior point QO method.

In particular, if constraints are disjoint and the rank of the set of linear

equality constraints is small, further reduction in computational costs can be

achieved, making it possible to apply this algorithm in high dimensional

weather forecasting problems.

Original language | English |
---|---|

Publisher | Arxiv |

Volume | 1909.04991 |

Publication status | Published - 12 Sep 2019 |

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### Cite this

*An algorithm for optimization with disjoint linear constraints and its application for predicting rain*. Arxiv.

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**An algorithm for optimization with disjoint linear constraints and its application for predicting rain.** / Janjic, Tijana; Ruckstuhl, Yvonne; Toint, Philippe.

Research output: Working paper

TY - UNPB

T1 - An algorithm for optimization with disjoint linear constraints and its application for predicting rain

AU - Janjic, Tijana

AU - Ruckstuhl, Yvonne

AU - Toint, Philippe

PY - 2019/9/12

Y1 - 2019/9/12

N2 - A specialized algorithm for quadratic optimization (QO, or, formerly, QP) withdisjoint linear constraints is presented. In the considered class ofproblems, a subset of variables are subject to linear equality constraints,while variables in a different subset are constrained to remain in a convex set.The proposed algorithm exploits the structure by combining steps in thenullspace of the equality constraint's matrix with projections onto the convex set. The algorithm is motivated by application in weather forecasting.Numerical results on a simple model designed for predicting rain show that thealgorithm is an improvement on current practice and that it reduces the computational burden compared to a more general interior point QO method.In particular, if constraints are disjoint and the rank of the set of linearequality constraints is small, further reduction in computational costs can beachieved, making it possible to apply this algorithm in high dimensionalweather forecasting problems.

AB - A specialized algorithm for quadratic optimization (QO, or, formerly, QP) withdisjoint linear constraints is presented. In the considered class ofproblems, a subset of variables are subject to linear equality constraints,while variables in a different subset are constrained to remain in a convex set.The proposed algorithm exploits the structure by combining steps in thenullspace of the equality constraint's matrix with projections onto the convex set. The algorithm is motivated by application in weather forecasting.Numerical results on a simple model designed for predicting rain show that thealgorithm is an improvement on current practice and that it reduces the computational burden compared to a more general interior point QO method.In particular, if constraints are disjoint and the rank of the set of linearequality constraints is small, further reduction in computational costs can beachieved, making it possible to apply this algorithm in high dimensionalweather forecasting problems.

M3 - Working paper

VL - 1909.04991

BT - An algorithm for optimization with disjoint linear constraints and its application for predicting rain

PB - Arxiv

ER -