An exact method for a discrete multiobjective linear fractional optimization

Integer linear fractional programming problem with multiple objective MOILFP is an important field of research and has not received as much attention as did multiple objective linear fractional programming. In this work, we develop a branch and cut algorithm based on continuous fractional optimization, for generating the whole integer efficient solutions of the MOILFP problem. The basic idea of the computation phase of the algorithm is to optimize one of the fractional objective functions, then generate an integer feasible solution. Using the reduced gradients of the objective functions, an efficient cut is built and a part of the feasible domain not containing efficient solutions is truncated by adding this cut. A sample problem is solved using this algorithm, and the main practical advantages of the algorithm are indicated.multiobjective programming, integer programming, linear fractional programming, branch and cut

Optimizing the size of array for modern discrete Fourier transform libraries

The problem of optimization of the array size for modern discrete Fourier transform libraries is considered and reformulated as an integer linear programming problem. Acceleration of finding an optimal solution using standard freely available library with respect to brute force approach is demonstrated. Ad hoc recursive algorithm of finding the optimal solution is proposed, complexity scaling of the algorithm is estimated analytically. The problem can be used in a linear programming class as an example of purely integer programming problem (continuous linear programming solution has no sense), simple enough to be solved using even interpreting programming languages like Python or Matlab.Comment: 12 pages, 1 table, submitte

An exact method for a discrete multiobjective linear fractional optimization

Integer linear fractional programming problem with multiple objective MOILFP is an important field of research and has not received as much attention as did multiple objective linear fractional programming. In this work, we develop a branch and cut algorithm based on continuous fractional optimization, for generating the whole integer efficient solutions of the MOILFP problem. The basic idea of the computation phase of the algorithm is to optimize one of the fractional objective functions, then generate an integer feasible solution. Using the reduced gradients of the objective functions, an efficient cut is built and a part of the feasible domain not containing efficient solutions is truncated by adding this cut. A sample problem is solved using this algorithm, and the main practical advantages of the algorithm are indicated

Reduction of Search-LWE Problem to Integer Programming Problem

Let $(A,t)$ be an instance of the search-LWE problem, where $A$ is a matrix and $t$ is a vector. This paper constructs an integer programming problem using $A$ and $t$, and shows that it is possible to derive a solution of the instance $(A,t)$ (perhaps with high probability) using its optimal solution or its tentative solution of small norm output by an integer programming solver. In other words, the LWE-search problem can be reduced to an integer programming problem. In the reduction, only basic linear algebra and finite field calculation are required. The computational complexity of the integer programming problem obtained is still unknown

The parallel approximability of a subclass of quadratic programming

In this paper we deal with the parallel approximability of a special class of Quadratic Programming (QP), called Smooth Positive Quadratic Programming. This subclass of QP is obtained by imposing restrictions on the coefficients of the QP instance. The Smoothness condition restricts the magnitudes of the coefficients while the positiveness requires that all the coefficients be non-negative. Interestingly, even with these restrictions several combinatorial problems can be modeled by Smooth QP. We show NC Approximation Schemes for the instances of Smooth Positive QP. This is done by reducing the instance of QP to an instance of Positive Linear Programming, finding in NC an approximate fractional solution to the obtained program, and then rounding the fractional solution to an integer approximate solution for the original problem. Then we show how to extend the result for positive instances of bounded degree to Smooth Integer Programming problems. Finally, we formulate several important combinatorial problems as Positive Quadratic Programs (or Positive Integer Programs) in packing/covering form and show that the techniques presented can be used to obtain NC Approximation Schemes for "dense" instances of such problems.Peer ReviewedPostprint (published version

Engineering Optimization: Methods/Applications - Colorado State University

This course provides a comprehensive treatment of methods of optimization with focus on linear programming and its extensions, network flow optimization, integer programming, quadratic programming, and an introduction to nonlinear programming. The goal is to maintain a balance between theory, numerical computation, problem setup for solution by computer algorithms, and engineering applications. Course taught at Colorado State University