An improved multi-parametric programming algorithm for flux balance analysis of metabolic networks

Flux balance analysis has proven an effective tool for analyzing metabolic networks. In flux balance analysis, reaction rates and optimal pathways are ascertained by solving a linear program, in which the growth rate is maximized subject to mass-balance constraints. A variety of cell functions in response to environmental stimuli can be quantified using flux balance analysis by parameterizing the linear program with respect to extracellular conditions. However, for most large, genome-scale metabolic networks of practical interest, the resulting parametric problem has multiple and highly degenerate optimal solutions, which are computationally challenging to handle. An improved multi-parametric programming algorithm based on active-set methods is introduced in this paper to overcome these computational difficulties. Degeneracy and multiplicity are handled, respectively, by introducing generalized inverses and auxiliary objective functions into the formulation of the optimality conditions. These improvements are especially effective for metabolic networks because their stoichiometry matrices are generally sparse; thus, fast and efficient algorithms from sparse linear algebra can be leveraged to compute generalized inverses and null-space bases. We illustrate the application of our algorithm to flux balance analysis of metabolic networks by studying a reduced metabolic model of Corynebacterium glutamicum and a genome-scale model of Escherichia coli. We then demonstrate how the critical regions resulting from these studies can be associated with optimal metabolic modes and discuss the physical relevance of optimal pathways arising from various auxiliary objective functions. Achieving more than five-fold improvement in computational speed over existing multi-parametric programming tools, the proposed algorithm proves promising in handling genome-scale metabolic models.Comment: Accepted in J. Optim. Theory Appl. First draft was submitted on August 4th, 201

Multi-parametric programming : novel theory and algorithmic developments

Imperial Users onl

A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty

Process and energy models provide an invaluable tool for design, analysis and optimisation. These models are usually based upon a number of assumptions, simplifications and approximations, thereby introducing uncertainty in the model predictions. Making model based optimal decisions under uncertainty is therefore a challenging task. This issue is further exacerbated when more than one objective is to be optimised simultaneously, resulting in a Multi-Objective Optimisation (MO2MO2) problem. Even though, some methods have been proposed for MO2MO2 problems under uncertainty, two separate optimisation techniques are employed; one to address the multi-objective aspect and another to take into account uncertainty. In the present work, we propose a unified optimisation framework for linear MO2MO2 problems, in which the uncertainty and the multiple objectives are modelled as varying parameters. The MO2MO2 under uncertainty problem (MO2U2)(MO2U2) is thus reformulated and solved as a multi-parametric programming problem. The solution of the multi-parametric programming problem provides the optimal solution as a set of parametric profiles

Learning the LMP-Load Coupling From Data: A Support Vector Machine Based Approach

This paper investigates the fundamental coupling between loads and locational marginal prices (LMPs) in security-constrained economic dispatch (SCED). Theoretical analysis based on multi-parametric programming theory points out the unique one-to-one mapping between load and LMP vectors. Such one-to-one mapping is depicted by the concept of system pattern region (SPR) and identifying SPRs is the key to understanding the LMP-load coupling. Built upon the characteristics of SPRs, the SPR identification problem is modeled as a classification problem from a market participant's viewpoint, and a Support Vector Machine based data-driven approach is proposed. It is shown that even without the knowledge of system topology and parameters, the SPRs can be estimated by learning from historical load and price data. Visualization and illustration of the proposed data-driven approach are performed on a 3-bus system as well as the IEEE 118-bus system

Multi-parametric Programming for Model Predictive Control

Model predictive control (MPC) solves a quadratic optimization problem to generate control law in each step. The usual methods of solution for quadratic optimization problem are interior point method, active set method etc. But most of the techniques are computationally heavy to perform the job in small amount of time. So a method is required where on-line computation is less. In multi-parametric quadratic programming (mp-QP) method an off-line computation is done a prior and a binary search tree is prepared. The on-line computation mainly involves a search through the binary-tree. The mp-QP is suitable for the class of optimization problem, where the objective function is to minimize or maximize a performance criterion subject to a given set of constraints where some of the parameter vary between lower and upper bounds. Also mp-QP is suitable for multi-objective optimization, where multi criteria problems can be reformulated as multi-parametric programming problems and a parametrized optimal solution is obtained. Multi-parametric programming is a technique for obtaining: (i) the objective and optimization variable as functions of the varying parameters and (ii) the regions in the space of the parameters where these functions are valid. The newly developed convex optimization solver CVXGEN is utilized successfully for off-line calculations which involves of dividing the parameter space into different polyhedral regions.In each one, the objective function has a constant value. The process involves another kind of optimization problem. For CVXGEN, worst case solving time is in milliseconds, even for a large problem.Thus, the use of CVXGEN minimizes the off-line calculation in mp-QP technique. In this work, an input constraint MPC problem is chosen from existing literature. The problem is solved for both two step prediction and three step prediction cases.The control input and states are ploted for both the MPC problems, and the results are compared

Design of multi-parametric NCO tracking controllers for linear dynamic systems

© 2016 The Authors.A methodology for combining multi-parametric programming and NCO tracking is presented in the case of linear dynamic systems. The resulting parametric controllers consist of (potentially nonlinear) feedback laws for tracking optimality conditions by exploiting the underlying optimal control switching structure. Compared to the classical multi-parametric MPC controller, this approach leads to a reduction in the number of critical regions. It calls for the solution of more difficult parametric optimization problems with linear differential equations embedded, whose critical regions are potentially nonconvex. Examples of constrained linear quadratic optimal control problems with parametric uncertainty are presented to illustrate the approach

STRUCTURAL OPTIMIZATION USING PARAMETRIC PROGRAMMING METHOD

The Parametric Programming method is investigated to consider its applicability to structural optimization problems. It is used to solve optimization problems that have design variables as implicit functions of some independent input parameter(s). It provides optimal solutions as a parametric function of the input parameter(s) for the entire parameter space of interest. It does not require the detailed discrete optimizations needed at a large number of parameter values as in traditional non-parametric optimization. Parametric programming is widely used in optimal controls, model predictive control, scheduling, process synthesis and material design under uncertainty due to the above mentioned benefits. Its benefits are however, still unexplored in the field of structural optimization. Parametric programming could for instance, be used to aid designers in identifying and optimizing for uncertain loading conditions in complex systems. The first objective of this thesis is to identify a suitable multi-parametric programming algorithm among the many available ones in the literature to solve structural optimization problems. Once selected, the second goal is to implement the chosen algorithm and solve single parametric and multi-parametric sizing optimization problems, shape optimization problems, and use multi-parametric programming as a multi-objective optimization tool in structural optimization. In this regard, sizing optimization of truss structures and shape optimization of beams for load magnitude and load directions as varying parameters are solved for single and multi-parameter static and/or dynamic load cases. Parametric programming is also used to solve the multi-objective optimization of a honeycomb panel and the results are compared with those from non-parametric optimization conducted using commercial optimization software. Accuracy of results, and computational time are considered. From these studies, inferences are drawn about the issues and benefits of using parametric programming in structural optimization