Side Constraints Research Articles

Price fluctuations and species diversity across fish markets in Kerala

The fish marketing system of Kerala deserves to be highly efficient as compared to that of the country. The demand-supplygap is ironed by the fish arrivals from the other states and there exists huge price variations of fish identified among thelanding centres, wholesale and retail markets of Kerala. The study analysed the price volatilities and species diversity,across different markets. Results of the study suggest that the supply side constraints and the monopsony conditions ofthe fish markets are the major factors responsible for the high price instability. The prices of low value fish species havenot been stable for several reasons and the prices varied depending on species, seasons and abundance of other fish andfishery products. The study suggests that the development of a Fish Market Price Information System (FMPIS) to act asa decision support system would ensure fish market and price information dissemination about availability, accessibilityand affordability of fish which also enables different stakeholders, mainly fishers in identifying target prices or markets;consumers with rational choices about fish availability and traders with inputs for better marketing efficiency. Keywords: Market diversity, Marketing efficiency, Price stability, Species diversity

Challenges and Strategic Solutions to Guarantee Last Mile Reach for an Indian TB Patient's Nikshay Poshan Yojana; A Conditional Cash Transfer Scheme Comment on "Does Direct Benefit Transfer Improve Outcomes Among People With Tuberculosis? - A Mixed-Methods Study on the Need for a Review of the Cash Transfer Policy in India".

India has put efforts into the prevention and control of tuberculosis (TB) for more than 50 years. Nikshay Poshan Yojna (NPY) is one of the schemes of conditional cash transfers (CCTs) by the Government of India. The CCT schemes mostly address the demand side constraints. Governments could use this in developing nations as a tool to divert financial resources toward societal development. In India, NPY is more directed toward providing monetary support for a nutritional diet and reducing the catastrophic expenditure of TB patients. Several studies highlighted challenges in implementing cash transfer schemes and provided different operational models. A country like India should address the challenges with defined strategies to ensure its last-mile reach. A present commentary discussing challenges and possible solutions that policy-makers can adapt and set up a support structure to ensure that supportive actions are implemented in response to patient and system side issues.

Optimization over decision trees: a case study for the design of stable direct-current electricity networks

In many real-world mixed-integer optimization problems from engineering, the side constraints can be subdivided into two categories: constraints which describe a certain logic to model a feasible allocation of resources (such as a maximal number of available assets, working time requirements, maintenance requirements, contractual obligations, etc.), and constraints which model physical processes and the related quantities (such as current, pressure, temperature, etc.). While the first type of constraints can often easily be stated in terms of a mixed-integer program (MIP), the second part may involve the incorporation of complex non-linearities, partial differential equations or even a black-box simulation of the involved physical process. In this work, we propose the integration of a trained tree-based classifier—a decision-tree or a random forest, into a mixed-integer optimization model as a possible remedy. We assume that the classifier has been trained on data points produced by a detailed simulation of a given complex process to represent the functional relationship between the involved physical quantities. We then derive MIP-representable reformulations of the trained classifier such that the resulting model can be solved using state-of-the-art solvers. At the hand of several use cases in terms of possible optimization goals, we show the broad applicability of our framework that is easily extendable to other tasks beyond engineering. In a detailed real-world computational study for the design of stable direct-current power networks, we demonstrate that our approach yields high-quality solutions in reasonable computation times.

Against the New Logical Argument from Evil

Jim Sterba’s Is a Good God Logically Possible? looks to resurrect J. L. Mackie’s logical argument from evil. Sterba accepts the general framework that theists seeking to give a theodicy have favored since Leibniz invented the term: the search for some greater good provided or greater evil averted that would justify God in permitting the type and variety of evil we actually observe. However, Sterba introduces a deontic twist, drawing on the Pauline Principle (let us not do evil that good may come) to introduce three deontic side constraints on God’s choice of action. He then splits the possible goods into four categories: first- vs. second-order goods, goods to which we have a right, and goods to which we do not have a right. He argues that his deontic constraints rule out each combination, thereby showing that no God-justifying good is on offer. To defuse the argument, I draw on a pair of ideas from Marilyn McCord Adams: (i) God is outside the bounds of morality, and (ii) God can defeat evils by incorporating them into an incommensurately valuable friendship with each human. Properly appreciated, these show that the new logical argument relies on a false premise that is not easily repaired.

Binary Control Pulse Optimization for Quantum Systems

Quantum control aims to manipulate quantum systems toward specific quantum states or desired operations. Designing highly accurate and effective control steps is vitally important to various quantum applications, including energy minimization and circuit compilation. In this paper we focus on discrete binary quantum control problems and apply different optimization algorithms and techniques to improve computational efficiency and solution quality. Specifically, we develop a generic model and extend it in several ways. We introduce a squared L2-penalty function to handle additional side constraints, to model requirements such as allowing at most one control to be active. We introduce a total variation (TV) regularizer to reduce the number of switches in the control. We modify the popular gradient ascent pulse engineering (GRAPE) algorithm, develop a new alternating direction method of multipliers (ADMM) algorithm to solve the continuous relaxation of the penalized model, and then apply rounding techniques to obtain binary control solutions. We propose a modified trust-region method to further improve the solutions. Our algorithms can obtain high-quality control results, as demonstrated by numerical studies on diverse quantum control examples.

A hybrid algorithm for large-scale non-separable nonlinear multicommodity flow problems

We propose an approach for large-scale non-separable nonlinear multicommodity flow problems by solving a sequence of subproblems which can be addressed by commercial solvers. Using a combination of solution methods such as modified gradient projection, shortest path algorithm and golden section search, the approach can handle general problem instances, including those with (i) non-separable cost, (ii) objective function not available analytically as polynomial but are evaluated using black-boxes, and (iii) additional side constraints not of network flow types. Implemented as a toolbox in commercial solvers, it allows researchers and practitioners, currently conversant with linear instances, to easily manage large-scale convex instances as well. In this article, we compared the proposed algorithm with alternative approaches in the literature, covering both theory and large test cases. New test cases with non-separable convex costs and non-network flow side constraints are also presented and evaluated. The toolbox is available free for academic use upon request.

Libertarianism, Defense of Property, and Absolute Rights

The present paper argues that libertarians (e.g. Murray Rothbard, Stephan Kinsella) who subscribe the proportionality principle while embracing the view that to have a right to property is to have a right to defend it run into what we call the Property Defense Dilemma. For if the only way to defend property is to defend it disproportionately, then a private property right – contrary to what these thinkers claim – is not accompanied by a right to defend it. The most plausible way out of the dilemma – the present paper argues – is to conceive of private property rights as only weakly absolute, to use Matthew H. Kramer’s illuminating distinction. On the other hand, libertarians who as Walter Block would like to escape the dilemma by replacing the proportionality standard with the gentleness principle run into other sorts of problems (moral implausibility, incoherence) which also shows that it is the libertarian view on rights as infinitely stringent side constraints that calls for revision and attenuation.

Expanding Access to Finance for Small and Medium Enterprises: An Analysis of Demand and Supply Side Constraints of Nepal

The objective of this article is to analyze the constraints on expanding access to finance with a special reference to Small and Medium Enterprises (SMEs) in Nepal. Following explorative research design, tools like review of literature, field observations, key informant interview with Banks and Financial Institutions (BFIs), and a quick questionnaire survey with SMEs in hilly districts of Nepal were deployed to collect data for this article. The descriptive statistics showed the financing gap very clearly. The constraints of SMEs on access to finance are found in policy and operational levels on both the demand and supply sides. There is a huge scope of modern technology to reduce the constraints towards expanding access to finance for SMEs in rural areas. The major constraints area includes the policy measures, lending approach and use of technology, rate of interest, availability of collateral with SMEs, risk-mitigating measures, the position of financial literacy, stage of SMEs’ market development and their business incomes, regulatory compliances, general price level, and development of the physical infrastructures.

Traffic Conditions and Characteristics: Investigation of Road Segment Performance

The dimensions of the road affect the flow of traffic passing over it. Even though the traffic lanes are the same width, the traffic flow will not be the same because the environment is different. This study aims to determine traffic conditions and characteristics through density analysis and the level of traffic service on the roads. This research is a type of non-experimental research, like a case study with survey methods and direct field observations to determine the performance of the road network. The primary data collected through direct recording in the field is the number of vehicles passing through Perintis Kemerdekaan Road and observing the side constraints that reduce capacity and service levels and impede smooth traffic flow. Data analysis used the Highway Capacity Manual (HCM) and Indonesia Road Capacity Manual (MKJI) 1997. The service level value (LOS) is obtained by dividing the peak traffic volume in the west direction of 4728 PCU/hour with the road capacity value obtained by the service level of 0.89 entering category E while in the east direction with a traffic volume value of 4974 PCU/hour obtained the degree of saturation value of 0.94. The results of the study show that the level of road service is between (D - E) with a value of 0.85 to 1.00, which means that it is close to road capacity and sluggish speed, high traffic density due to internal barriers and vehicle drivers who experience traffic jams of medium duration.

Determinants of Export Performance of Pakistan: Evidence from the Firm-Level Data

This paper explores the determinants of export performance at the level of firms in respect of their characteristics and supply side constraints. The analysis is based on a survey of export-oriented firms in four major sectors. The results indicate a relationship between the better performance of foreign-owned firms to their better know-how and resources compared to the domestically owned firms. Export performance is positively affected by the level of investment in market/client oriented technologies. Lack of certification of product and process standards is the main supply side constraint adversely affecting the firms’ export performance. Facilitation measures like export processing zones, internationally recognised testing labs, and industrial clusters would be helpful in improving the export performance of firms. JEL classification: F1, L1, L6 Keywords: Trade, Exports, Firms, Performance, Manufacturing

Formally Robust and Safe Trajectory Planning and Tracking for Autonomous Vehicles

In this paper, a safe trajectory planning and tracking algorithm for autonomous vehicles is proposed. Specially, the safety problem considers the geometric constraints including the obstacle avoiding and the road side constraints, and the non-convex input constraints defined from the sideslip angle of the wheels and input boundedness. Control barrier function (CBF) is adopted to deal with the state and input constraints and generate nominal trajectory. For this purpose, the nominal dynamics of the autonomous vehicle is defined as the virtual dynamics, from which the CBF safety certificates are derived. By constructing appropriate feedback control, the tracking error of the actual trajectory can be bounded into a tube, which guarantees the geometric safety of the actual vehicle. Two safety certificates, the bearing and the distance safety certificates are derived for multiple-obstacle avoidance. In order to deal with the non-convex input constraints, a safe braking maneuver is carefully considered. The feasible initial velocity set for safe braking is proposed as a part of state constraints. The feasibility and the safety of the overall system is proved. The algorithm to synthesis the CBF certificates for multiple-obstacle avoidance, and the input constraints is proposed. Simulation results from an autonomous vehicle including disturbances demonstrate the feasibility of the algorithm. The software implementation of the proposed algorithm was developed in C++ intended for real-world testing.

The new electric SUV market under battery supply constraints: Might they increase CO2 emissions?

Two trends have shaped the European car market over the past decade: electrification and SUVisation. Recently, they have merged in a new product: the electric sport utility vehicle. This paper contrasts the view that regards this innovation as a technological solution to mitigate road transport emissions with a broader view that takes into account the battery requirement and profitability of such type of electric car. This broad view is formalised in a system dynamics model that captures the potential effects of supply side constraints. Four scenarios are simulated. The results show that the levels of CO2 emissions of new cars are substantially reduced only in the one that assumes lower motorisation. Based on these simulations, the conclusion that the diffusion of electric sport utility vehicles does not necessarily contribute to mitigating car emissions in Europe is drawn. Less reliance on technology fixes, downsizing and reduced motorisation are identified as solutions worth investigating to meet ambitious climate goals. Further research on how vehicle manufacturers distribute batteries is needed.

Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems

Semidefinite programming is a powerful tool in the design and analysis of approximation algorithms for combinatorial optimization problems. In particular, the random hyperplane rounding method of Goemans and Williamson [ 31 ] has been extensively studied for more than two decades, resulting in various extensions to the original technique and beautiful algorithms for a wide range of applications. Despite the fact that this approach yields tight approximation guarantees for some problems, e.g., Max-Cut , for many others, e.g., Max-SAT and Max-DiCut , the tight approximation ratio is still unknown. One of the main reasons for this is the fact that very few techniques for rounding semi-definite relaxations are known. In this work, we present a new general and simple method for rounding semi-definite programs, based on Brownian motion. Our approach is inspired by recent results in algorithmic discrepancy theory. We develop and present tools for analyzing our new rounding algorithms, utilizing mathematical machinery from the theory of Brownian motion, complex analysis, and partial differential equations. Focusing on constraint satisfaction problems, we apply our method to several classical problems, including Max-Cut , Max-2SAT , and Max-DiCut , and derive new algorithms that are competitive with the best known results. To illustrate the versatility and general applicability of our approach, we give new approximation algorithms for the Max-Cut problem with side constraints that crucially utilizes measure concentration results for the Sticky Brownian Motion, a feature missing from hyperplane rounding and its generalizations.

Stability Representations of Many-to-One Matching Problems: An Integer Optimization Approach

We consider integer optimization models for finding stable solutions to many-to-one, utility-weighted matching problems with incomplete preference lists and ties. Whereas traditional algorithmic approaches for the stable many-to-one matching problem, such as the deferred acceptance algorithm, offer efficient performance for the strict problem setting, adaptation to alternative settings often requires careful customization. Optimization-based approaches are free of the need to create customized algorithms for each unique context and can readily accommodate such extensions as (incomplete) preference lists with ties, alternative and nontraditional objective functions, and side constraints including those that ensure stable matching outcomes free of waste. We explore the flexibility of optimization-based approaches in several ways. First, we introduce four new constraint sets that prevent justified envy and a new system of constraints that prevents waste; taken together, they jointly ensure stable matching outcomes. Second, we create two algorithms to accelerate the generation of our proposed constraints. Third, we construct aggregate objective functions to reflect multiple hierarchical emphases by imposing a strict lexicographical order on the individual components. Fourth, we conduct comprehensive experiments to study the computational performance of our proposed optimization models and compare them with models from the extant literature under a variety of problem attributes. Our experiments reveal the circumstances under which each stability representation excels in terms of optimality criteria and computational efficiency on a variety of real and synthetic data sets. One such setting in which our proposed stability representations excel includes the important context of when sufficient seats exist for applicants, such as school choice problems and hospital residency matching. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information [ https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1237 ] or is available from the IJOC GitHub software repository ( https://github.com/INFORMSJoC ) at [ http://dx.doi.org/10.5281/zenodo.6892615 ].

Fostering Green Finance for Sustainable Development: A Focus on Textile and Leather Small Medium Enterprises in Pakistan

The green financing landscape in Pakistan is evolving, which signifies the need for a comprehensive gap analysis that examines the present status and constraints in supporting green finance in the country. Textile and leather industries are key sectors in Pakistan’s economy and rely heavily on small and medium enterprises (SMEs). Excessive resource use and inadequate environmental management methods pose a significant danger to the sectors’ long-term viability and account for an extreme relevance to the embracement of the circular economy paradigm. Green finance aims to ensure that economic process, safeguarding the environment, and maintaining environmental integrity all grow together. This research used a literature review and interview-based methodology (in which we interviewed more than 20 people representing SME operators, government officials, and banking staff) to examine SME finance, green banking, and demand and supply side constraints to cleaner/sustainable manufacturing in Pakistan’s leather and textile sectors. The research findings show that policy uncertainty and financial short-termism are economic impediments and obstacles that constitute a path-dependent, lock-in, non-linear mechanism. This study found a lack of tailored business advisory and financing for SMEs to learn about and invest in sustainable consumption and production (SCP). Furthermore, many banks in Pakistan also show a strong commitment to the expansion of the State Bank of Pakistan’s Green Banking Guidelines (GBGs). The results also highlight the high value of government assistance for businesses participating in green initiatives and incentives for banking institutions and private limited companies to support and invest in green practices.

Evacuation route planning for alternative fuel vehicles

As the number of adopted alternative fuel vehicles increases, it is crucial for communities (especially those that are susceptible to hazards) to make evacuation plans that account for such vehicles refueling needs. During emergencies that require preemptive evacuation planning, travelers using alternative fuel vehicles are vulnerable when evacuation routes do not provide access to refueling stations on their way to shelters. In this paper, we formulate and solve a novel seamless evacuation route plan problem, by designing |K| spanning trees with side constraints that capture the refueling needs of each k∈K vehicle fuel type on their way to reach a shelter. We develop a branch-and-price matheuristic algorithm based on column generation to solve the evacuation route planning problem. We apply the proposed framework to the Sioux Falls transportation network with existing infrastructure deployment and present numerical experiments. Specifically, we discuss the optimal system evacuation travel and refueling times under scenarios of various alternative fuel vehicle driving ranges. Our findings show that the characteristics of each vehicle fuel type, like driving range and the refueling infrastructure topology, play a pivotal role in determining evacuation route plans. This means that an evacuation route could prove unique to a single vehicle fuel type, while being infeasible to others. Finally, we observe that the driving range constraints could force evacuee vehicles to detour to meet their refueling needs before reaching safety and increase the total evacuation time by 7.32 % in one of evaluated scenarios.

A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS)

Feature selection reduces the complexity of high-dimensional datasets and helps to gain insights into systematic variation in the data. These aspects are essential in domains that rely on model interpretability, such as life sciences. We propose a (U)ser-Guided (Bay)esian Framework for (F)eature (S)election, UBayFS, an ensemble feature selection technique embedded in a Bayesian statistical framework. Our generic approach considers two sources of information: data and domain knowledge. From data, we build an ensemble of feature selectors, described by a multinomial likelihood model. Using domain knowledge, the user guides UBayFS by weighting features and penalizing feature blocks or combinations, implemented via a Dirichlet-type prior distribution. Hence, the framework combines three main aspects: ensemble feature selection, expert knowledge, and side constraints. Our experiments demonstrate that UBayFS (a) allows for a balanced trade-off between user knowledge and data observations and (b) achieves accurate and robust results.

Are the folk utilitarian about animals?

Robert Nozick famously raised the possibility that there is a sense in which both deontology and utilitarianism are true: deontology applies to humans while utilitarianism applies to animals. In recent years, there has been increasing interest in such a hybrid views of ethics. Discussions of this Nozickian Hybrid View, and similar approaches to animal ethics, often assume that such an approach reflects the commonsense view, and best captures common moral intuitions. However, recent psychological work challenges this empirical assumption. We review evidence suggesting that the folk is deontological all the way down—it is just that the moral side constraints that protect animals from harm are much weaker than those that protect humans. In fact, it appears that people even attribute some deontological protections, albeit extremely weak ones, to inanimate objects. We call this view Multi-level Weighted Deontology. While such empirical findings cannot show that the Nozickian Hybrid View is false, or that it is unjustified, they do remove its core intuitive support. That support belongs to Multi-level Weighted Deontology, a view that is also in line with the view that Nozick himself seemed to favour. To complicate things, however, we also review evidence that our intuitions about the moral status of humans are, at least in significant part, shaped by factors relating to mere species membership that seem morally irrelevant. We end by considering the potential debunking upshot of such findings about the sources of common moral intuitions about the moral status of animals.

The Nonconsequentialist Argument from Evil

Stringent non-consequentialist constraints on permitting horrendous evils pose a formidable challenge to the project of theodicy by limiting the ways in which it is permissible for God to do or allow evil for the sake of bringing about a greater good. I formulate a general and potent argument against all greater-good theodicies (which includes most theodicies) from the existence of robust side constraints on permitting evil. Then I contend that the argument fails. I begin by distinguishing between side constraints on doing evil and side constraints on allowing evil, and then I draw on the work of David Lewis and Fiona Woollard to argue that, because of the unique ways that God is related to the world, it is plausible that God can both create and sustain our universe, despite all of its evils, without violating any of these constraints.

Weight Constrained Path Finding with Bidirectional A*

Weight constrained path finding, known as a challenging variant of the classic shortest path problem, aims to plan cost optimum paths whose weight/resource usage is limited by a side constraint. Given the bi-criteria nature of the problem (i.e., the presence of cost and weight), solutions to the Weight Constrained Shortest Path Problem (WCSPP) have some properties in common with bi-objective search. This paper leverages the state-of-the-art bi-objective search algorithm BOBA* and presents WC-BA*, an exact A*-based WCSPP method that explores the search space in different objective orderings bidirectionally. We also enrich WC-BA* with two novel heuristic tuning approaches that can significantly reduce the number of node expansions in the exhaustive search of A*. The results of our experiments on a large set of realistic problem instances show that our new algorithm solves all instances and outperforms the state-of-the-art WCSPP algorithms in various scenarios.