21 papers · ranked by Valyu relevance
Krivulin, Nikolai
We consider discrete best approximation problems in the setting of tropical algebra that is concerned with the theory and application of algebraic systems with idempotent operations. Given a set of input-output pairs of an unknown function defined on a tropical semifield, the problem is to determine an approximating…
Yair Censor, Walaa M. Moursi, Tyler Weames, Henry Wolkowicz
We consider the problem of finding the best approximation point from a polyhedral set, and its applications, in particular to solving large-scale linear programs. The classical best approximation problem has many various solution techniques as well as applications. We study a regularized nonsmooth Newton type solution…
Nikolai Krivulin
We introduce new discrete best approximation problems, formulated and solved in the framework of tropical algebra, which deals with semirings and semifields with idempotent addition. Given a set of samples, each consisting of the input and output of an unknown function defined on an idempotent semifield, the problem is…
Mariano Rodríguez-Arias Fernández, Javier Cabello Sánchez, Juan Antonio Fernández Torvisco
Given some data t = (t1, . . . , tn) ∈ R n , T = (T1, . . . , Tn) ∈ R n , in this paper we are going to show how to determine the coefficients a, b, k ∈ R that make the exponential f(t) = a exp(kt) + b minimize the error
Vladimir Yu. Protasov, Rinat Kamalov
We address the problem of the best uniform approximation of a continuous function on a convex domain. The approximation is by linear combinations of a finite system of functions (not necessarily Chebyshev) under arbitrary linear constraints. By modifying the concept of alternance and of the Remez iterative procedure we…
Peiping Shen, Chunfeng Wang
This paper presents a linear decomposition approach for a class of nonconvex programming problems by dividing the input space into polynomially many grids. It shows that under certain assumptions the original problem can be transformed and decomposed into a polynomial number of equivalent linear programming…
Peiping Shen, Tongli Zhang, Chunfeng Wang
This article presents a new approximation algorithm for globally solving a class of generalized fractional programming problems (P) whose objective functions are defined as an appropriate composition of ratios of affine functions. To solve this problem, the algorithm solves an equivalent optimization problem (Q) via an…
Saeed Asadi Bagloee, Majid Sarvi, Yong Deng
Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of…
Jimmy Wu, Alex Khodaverdian, Benjamin Weitz, Nir Yosef
Network connectivity problems are abundant in computational biology research, where graphs are used to represent a range of phenomena: from physical interactions between molecules to more abstract relationships such as gene co-expression. One common challenge in studying biological networks is the need to extract…
Fabian Fröhlich, Peter K. Sorger
Ordinary differential equation (ODE) models are widely used to describe biochemical processes, since they effectively represent mass action kinetics. Optimization-based calibration of ODE models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a…
Kapil Devkota, Anselm Blumer, Lenore Cowen, Xiaozhe Hu
A well-studied approximate version of the graph matching problem is directly relevant for the study of protein-protein interaction networks. Called by the computational biology community Global Network Alignment, the two networks to be matched are derived from the protein-protein interaction (PPI) networks from…
Peter L. Bartlett, Chris Junchi Li, Jingfeng Wu, Bin Yu
In the field of optimization, developing accelerated methods for solving minimax and fixed-point problems remains a fundamental challenge. This paper presents a novel family of dual accelerated algorithms that achieve optimal convergence rates for both minimax and fixed-point problems. By exploring new anchoring…
Mohammad Dehghani, Eva Trojovská, Pavel Trojovský, Om Parkash Malik + 1 more
'Huiling Chen'] This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while…
Sergio Garcia, Cong Trinh
A large space of chemicals with broad industrial and consumer applications could be synthesized by engineered microbial biocatalysts. However, the current strain optimization process is prohibitively laborious and costly to produce one target chemical and often requires new engineering efforts to produce new molecules.…
Ahmed F. Ali, Mohamed A. Tawhid
Cuckoo search algorithm is a promising metaheuristic population based method. It has been applied to solve many real life problems. In this paper, we propose a new cuckoo search algorithm by combining the cuckoo search algorithm with the Nelder-Mead method in order to solve the integer and minimax optimization…
Ireneusz Gościniak, Krzysztof Gdawiec
There is a huge group of algorithms described in the literature that iteratively find solutions of a given equation. Most of them require tuning. The article presents root-finding algorithms that are based on the Newton-Raphson method which iteratively finds the solutions, and require tuning. The modification of the…
Muhammad Farman, Muhammad Farhan Tabassum, Muhammad Saeed, Nazir Ahmad Chaudhry
Hepatitis B is the main public health problem of the whole world. In epidemiology, mathematical models perform a key role in understanding the dynamics of infectious diseases. This paper proposes Padé approximation (Pa) with Differential Evolution (DE) for obtaining solution of Hepatitis-B model which is nonlinear…
Sabyasachi Shivkumar, Madeline S. Cappelloni, Ross K. Maddox, Ralf M. Haefner
Perceptual decision-making has been extensively modeled using the ideal observer framework. However, a range of deviations from optimality demand an extension of this framework to characterize the different sources of suboptimality. Prior work has mostly formalized these sources by adding biases and variability in the…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Thomas Lynn, Julio Ottino, Richard Lueptow, Paul Umbanhowar
Cut-and-shuffle mixing is an instructive candidate system with which to assess the potential of machine learning (ML) as an approach to solve difficult mixing problems. We focus on a specific subset of cut-and-shuffle systems, the one-dimensional interval exchange transform. This class of mixing operations is well…
Mojtaba Ghasemi, Abolfazl Rahimnejad, Ebrahim Akbari, Ravipudi Venkata Rao + 4 more
'Ravipudi Venkata Rao' 'Pavel Trojovský' 'Eva Trojovská' 'Stephen Andrew Gadsden' 'Yilun Shang'] Many important engineering optimization problems require a strong and simple optimization algorithm to achieve the best solutions. In 2020, Rao introduced three non-parametric algorithms, known as Rao algorithms, which have…