A step towards transparent integration of input-consciousness into dynamic program optimizations

Accurately predicting program behaviors (e.g., memory locality, method calling frequency) is fundamental for program optimizations and runtime adaptations. Despite decades of remarkable progress, prior studies have not systematically exploited the use of program inputs, a deciding factor of program behaviors, to help in program dynamic optimizations. Triggered by the strong and predictive correlations between program inputs and program behaviors that recent studies have uncovered, the dissertation work aims to bring program inputs into the focus of program behavior analysis and program dynamic optimization, cultivating a new paradigm named input-centric program behavior analysis and dynamic optimization.;The new optimization paradigm consists of three components, forming a three-layer pyramid. at the base is program input characterization, a component for resolving the complexity in program raw inputs and extracting important features. In the middle is input-behavior modeling, a component for recognizing and modeling the correlations between characterized input features and program behaviors. These two components constitute input-centric program behavior analysis, which (ideally) is able to predict the large-scope behaviors of a program's execution as soon as the execution starts. The top layer is input-centric adaptation, which capitalizes on the novel opportunities created by the first two components to facilitate proactive adaptation for program optimizations.;This dissertation aims to develop this paradigm in two stages. In the first stage, we concentrate on exploring the implications of program inputs for program behaviors and dynamic optimization. We construct the basic input-centric optimization framework based on of line training to realize the basic functionalities of the three major components of the paradigm. For the second stage, we focus on making the paradigm practical by addressing multi-facet issues in handling input complexities, transparent training data collection, predictive model evolvement across production runs. The techniques proposed in this stage together cultivate a lifelong continuous optimization scheme with cross-input adaptivity.;Fundamentally the new optimization paradigm provides a brand new solution for program dynamic optimization. The techniques proposed in the dissertation together resolve the adaptivity-proactivity dilemma that has been limiting the effectiveness of existing optimization techniques. its benefits are demonstrated through proactive dynamic optimizations in Jikes RVM and version selection using IBM XL C Compiler, yielding significant performance improvement on a set of Java and C/C++ programs. It may open new opportunities for a broad range of runtime optimizations and adaptations. The evaluation results on both Java and C/C++ applications demonstrate the new paradigm is promising in advancing the current state of program optimizations.

This chapter advocates a nonsmooth differential-algebraic equations (DAEs) modeling paradigm for dynamic simulation and optimization of continuous pharmaceutical process operations. Process intensification of pharmaceuticals production by continuous manufacturing is challenged by short campaigns, due to the low volume of some products. This calls for optimization of the entire dynamic campaign to achieve the highest overall yields, productivity and minimize waste. A variety of processes prevailing in the pharmaceutical industry are traditionally viewed as exhibiting hybrid continuous and discrete (i.e. discontinuous) behavior. In many cases such behavior is actually nonsmooth, rather than discontinuous. For such cases a distinction has not always been made, and these can be readily modeled by nonsmooth DAEs. A computationally relevant theory of nonsmooth DAEs (i.e. well-posedness and sensitivity analysis) has recently been established which is suitable for numerical implementations that scale efficiently for large-scale dynamic optimization problems.This chapter gives a brief overview of the nonsmooth DAEs framework. Such a formulation has been recently shown to admit a unique solution and computationally tractable sensitivity analysis, facilitating its embedding in optimization schemes to find optimal operational procedures. The mathematical modeling of selected processes using nonsmooth DAEs is outlined in detail. The nonsmooth dynamics of several units are illustrated. We then demonstrate how the overall campaign performance can be optimized in terms of on-specification productivity and yield, which are considered as objectives given a short time horizon for the production campaign. The optimal solutions give very valuable insights regarding the best start-up and shutdown procedures.

Nonsmooth differential-algebraic equations in chemical engineering

A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems

Many real-world optimization problems involve multiple objectives, constraints, and parameters that may change over time. These problems are often called dynamic multiobjective optimization problems (DMOPs). The difficulty in solving DMOPs is the need to track the changing Pareto-optimal front efficiently and accurately. It is known that transfer learning (TL)-based methods have the advantage of reusing experiences obtained from past computational processes to improve the quality of current solutions. However, existing TL-based methods are generally computationally intensive and thus time consuming. This article proposes a new memory-driven manifold TL-based evolutionary algorithm for dynamic multiobjective optimization (MMTL-DMOEA). The method combines the mechanism of memory to preserve the best individuals from the past with the feature of manifold TL to predict the optimal individuals at the new instance during the evolution. The elites of these individuals obtained from both past experience and future prediction will then constitute as the initial population in the optimization process. This strategy significantly improves the quality of solutions at the initial stage and reduces the computational cost required in existing methods. Different benchmark problems are used to validate the proposed algorithm and the simulation results are compared with state-of-the-art dynamic multiobjective optimization algorithms (DMOAs). The results show that our approach is capable of improving the computational speed by two orders of magnitude while achieving a better quality of solutions than existing methods.

Contributors

Research on energy management strategy for fuel cell hybrid electric vehicles based on improved dynamic programming and air supply optimization

To solve the problem of mixed integer nonlinear programming in dynamic reactive power optimization of distribution networks, a two-stage dynamic reactive power optimization model is used to reduce the size of the reactive power optimization calculation.The first stage is nonlinear planning, with the objective of minimizing the combined system network loss and node voltage offset. The second stage is a mixed-integer planning, with the objective of minimizing the incremental power system network loss on the basis of satisfying the constraints such as the number of equipment regulation throughout the day. In view of the fact that the conventional penalty function method to deal with the constraints will result in improper selection of penalty factors, an improved quantum state transfer algorithm with mixed filter technique is proposed. The algorithm adopts a two-population parallel search approach, and the individual adaptation degree and constraint violation degree are constituted as filter pairs as evaluation metrics to replace the penalty functions. Finally, the proposed algorithm, the traditional state transfer algorithm and Colony Algorithm are solved separately for the model using the IEEE 14 system as examples, and the correctness of the proposed model and algorithm is verified by comparing and analyzing the simulation results.

Reservoir optimal scheduling is a typical multi-constrained,dynamic,non-linear optimization problem.To solve this problem,a Dynamic Programming-Particle Swarm Optimization (DP-PSO) algorithm was used to solving.This algorithm used the optimal Dynamic Programming (DP) principle to convert the reservoir optimal scheduling problem to multistage decision-making sub-problems;the solution of each sub-problem was got by particle swarm optimization algorithm.The numerical experiments show that with more time in calculation,the reliability of the DP-PSO is superior to the general DP algorithm,and the calculation time of DP-PSO is less than DP-Genetic Algorithm (DP-GA).

The amount of data transferred over dedicated and non-dedicated network links has been increasing much faster than the increase in the network capacity. On the other hand, the current data transfer solutions fail to guarantee even the promised achievable transfer throughput. In this article, we propose a novel two-phase dynamic throughput optimization model based on mathematical modeling with offline knowledge discovery/analysis and adaptive online decision making. In the offline analysis, we mine historical transfer logs to perform knowledge discovery about the transfer characteristics. The online phase uses the discovered knowledge from the offline analysis along with the real-time investigation of the network condition to optimize the protocol parameters. As the real-time investigation is expensive and provides partial knowledge about the current network status, our model uses historical knowledge about the network and data characteristics to reduce the real-time investigation overhead while ensuring near-optimal throughput for each transfer. Our novel approach is tested over different networks with different datasets, and it has outperformed its closest competitor by 1.7x and the default case by 5x. It also achieved up to 93 percent accuracy compared to the optimal achievable throughput possible on those networks.

The following sections are included:IntroductionIntegrationBasic StatisticsBasic Linear AlgebraStatic OptimizationNotes on Stochastic Difference EquationsDynamic Optimization in Discrete Time: Heuristics of Dynamic Programming in the Certainty CaseStochastic Dynamic Optimization in Discrete TimeNotes on Stochastic Differential EquationsStochastic Dynamic Optimization in Continuous TimeSummaryProblemsNotes on the LiteratureReferences

The Irrigation Area (D. I.) Mambal, which passes through Badung Regency, Denpasar City, and Tabanan Regency, is the largest irrigation water supplied by the Ayung River, covering an area of 5.963 Ha. Despite the Ayung River’s substantial water potential, the D. I. Mambal experiences water shortages during certain months. This research aims to improve the effectiveness and efficiency of irrigation water use based on the Global Planting Management Plan (RTTG) using simulation methods and dynamic program optimization. Simulations were carried out under low conditions, normal and sufficient dependable discharges, using both existing and alternative RTTG. The objective function of the dynamic optimization seeks to maximize revenue gain from the applied RTTG. The existing cropping pattern at the beginning of planting in October showed an average proportion of fulfillment of water irrigation needs at 85%. Under the Alternative I condition, with planting beginning in November, the average proportion of fulfillment of irrigation water needs was 89%. In Alternative II conditions, with planting beginning in December, the average proportion of fulfillment of irrigation water needs was 87%. By optimizing the water discharge using the dynamic program, the irrigation profit for the existing cropping pattern (October) amounted to IDR 491,816,154,938. The highest profit was obtained using the Alternative II cropping pattern (December), totaling IDR 606,675,369,830. Meanwhile, the lowest profit was obtained in the Alternative I cropping pattern (November), which was IDR 360,767,292,361. The analysis showed that the Alternative II cropping pattern, starting with the first rice planting period in December, yields the most optimal results. The analysis considers the optimized air allocation and irrigation benefits obtained from the third cropping pattern.

Online travel platforms have emerged as critical components of the digital economy, transforming how people plan and book their travels while presenting complex technological challenges. These platforms must address various technical hurdles, including multi-tenant architecture management, dynamic pricing systems, search optimization, and scalability requirements. The article explores key challenges faced by modern travel platforms and provides comprehensive solutions for overcoming them. It examines infrastructure and integration challenges, particularly in API management, payment processing, and data security. The discussion extends to user experience considerations, focusing on cross-platform consistency and personalization strategies. The article also covers performance optimization techniques, including database management, application performance, and monitoring systems. Through a detailed examination of these challenges and their solutions, the article provides a framework for building robust and efficient travel platforms that meet contemporary industry demands.

In this dissertation we develop numerical algorithms and software tools to facilitate parallel solutions of nonlinear programming (NLP) problems. In particular, we address large-scale, block-structured problems with an intrinsic decomposable configuration. These problems arise in a great number of engineering applications, including parameter estimation, optimal control, network optimization, and stochastic programming. The structure of these problems can be leveraged by optimization solvers to accelerate solutions and overcome memory limitations, and we propose variants to two classes of optimization algorithms: augmented Lagrangian (AL) schemes and Schur-complement interior-point methods. The convergence properties of augmented Lagrangian decomposition schemes like the alternating direction method of multipliers (ADMM) and progressive hedging (PH) are well established for convex optimization but convergence guarantees in non-convex settings are still poorly understood. In practice, however, ADMM and PH often perform satisfactorily in complex non-convex NLPs. In this work, we study connections between the method of multipliers (MM), ADMM, and PH to derive benchmarking metrics that explain why PH and ADMM work in practice. We illustrate the concepts using challenging dynamic optimization problems. Our exposition seeks to establish more formalism in benchmarking ADMM, PH, and AL schemes and to motivate algorithmic improvements. The effectiveness of nonlinear interior-point solvers for solving large-scale problems relies quite heavily on the solution of the underlying linear algebra systems. The schur-complement decomposition is very effective for parallelizing the solution of linear systems with modest coupling. However, for systems with large number of coupling variables the schur-complement method does not scale favorably. We implement an approach that uses a Krylov solver (GMRES) preconditioned with ADMM to solve block-structured linear systems that arise in the interior-point method. We show that this ADMM-GMRES approach overcomes the well-known scalability issues of Schur decomposition. One important drawback of using decomposition approaches like ADMM and PH is their convergence rate. Unlike Schur-complement interior-point algorithms that have super-linear convergence, augmented Lagrangian approaches typically exhibit linear and sublinear rates. We exploit connections between ADMM and the Schur-complement decomposition to derive an accelerated version of ADMM. Specifically, we study the effectiveness of performing a Newton-Raphson algorithm to compute multiplier estimates for augmented Lagrangian methods. We demonstrate using two-stage stochastic programming problems that our multiplier update achieves convergence in fewer iterations for MM on general nonlinear problems. In the case of ADMM, the newton update significantly reduces the number of subproblem solves for convex quadratic programs (QPs). Moreover, we show that using newton multiplier updates makes the method robust to the selection of the penalty parameter. Traditionally, state-of-the-art optimization solvers are implemented in low-level programming languages. In our experience, the development of decomposition algorithms in these frameworks is challenging. They present a steep learning curve and can slow the development and testing of new numerical algorithms. To mitigate these challenges, we developed PyNumero, a new open source framework implemented in Python and C++. The package seeks to facilitate development of optimization algorithms for large-scale optimization within a high-level programming environment while at the same time minimizing the computational burden of using Python. The efficiency of PyNumero is illustrated by implementing algorithms for problems arising in stochastic programming and optimal control. Timing results are presented for both serial and parallel implementations. Our computational studies demonstrate that with the appropriate balance between compiled code and Python, efficient implementations of optimization algorithms are achievable in these high-level languages.

Chapter 10 - Power Limits in Thermochemical Units, Fuel Cells, and Separation Systems