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
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
On the continuous quadratic knapsack problem
Mathematical Programming: Series A and B
Dynamic Programming: Models and Applications
Dynamic Programming: Models and Applications
Numerical Recipes: FORTRAN
Algorithms for Separable Nonlinear Resource Allocation Problems
Operations Research
Operations Research Letters
Quadratic resource allocation with generalized upper bounds
Operations Research Letters
Exact Algorithm for Concave Knapsack Problems: Linear Underestimation and Partition Method
Journal of Global Optimization
Journal of Global Optimization
Journal of Computational and Applied Mathematics
Convergent Lagrangian and domain cut method for nonlinear knapsack problems
Computational Optimization and Applications
The design of optimum component test plans for system reliability
Computational Statistics & Data Analysis
Capacity-constrained multiple-market price discrimination
Computers and Operations Research
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In this paper we present a new algorithm for solving the nonlinear resource allocation problem. The nonlinear resource allocation problem is defined as the minimization of a convex function over a single convex constraint and bounded integer variables. We first present a pegging algorithm for solving the continuous variable problem, and then incorporate the pegging method in a branch and bound algorithm for solving the integer variable problem. We compare the computational performance of the pegging branch and bound algorithm with three other methods: a multiplier search branch and bound algorithm, dynamic programming, and a 0,1 linearization method. The computational results demonstrate that the pegging branch and bound algorithm advances the state of the art in methods for solving the nonlinear resource allocation problem.