Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds
Mathematical Programming: Series A and B
On the continuous quadratic knapsack problem
Mathematical Programming: Series A and B
About strongly polynomial time algorithms for quadratic optimization over submodular constraints
Mathematical Programming: Series A and B
Algorithms for Network Programming
Algorithms for Network Programming
Linear time algorithms for some separable quadratic programming problems
Operations Research Letters
A branch and search algorithm for a class of nonlinear knapsack problems
Operations Research Letters
Review of recent development: An O( n) algorithm for quadratic knapsack problems
Operations Research Letters
A surrogate relaxation based algorithm for a general quadratic multi-dimensional knapsack problem
Operations Research Letters
A pegging algorithm for the nonlinear resource allocation problem
Computers and Operations Research
Uplink cross-layer scheduling with differential QoS requirements in OFDMA systems
EURASIP Journal on Wireless Communications and Networking - Special issue on adaptive cross-layer strategies for fourth generation wireless communications
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In this paper we present an algorithm for solving a quadratic resource allocation problem that includes a set of generalized upper bound (GUB) constraints. The problem involves minimizing a quadratic function over one linear constraint, a set of GUB constraints, and bounded variables. GUB constraints, when added to a standard resource allocation problem, can be used to set upper limits on the amount of a resource consumed by one or more subsets of the activities. To solve the problem, we present an efficient algorithm that solves a series of quadratic knapsack subproblems and box constrained quadratic subproblems. Computational results are reported for large-scale problems with as many as 100 000 variables and 1000 constraints. The computational results indicate that our algorithm is up to 4000 times faster than the general purpose nonlinear programming software LSGRG.