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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An algorithm for optimization with disjoint linear constraints and its application for predicting rain. / Janjic, Tijana; Ruckstuhl, Yvonne; Toint, Philippe.
Arxiv, 2019.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 -