Let's talk: public evaluation of large projects: variational inequialities, bilevel programming and complementarity. a survey

Large projects evaluation rises well known difficulties because -by definition- they modify the current price system; their public evaluation presents additional difficulties because they modify too existing shadow prices without the project. This paper analyzes -first- the basic methodologies applied until late 80s., based on the integration of projects in optimization models or, alternatively, based on iterative procedures with information exchange between two organizational levels. New methodologies applied afterwards are based on variational inequalities, bilevel programming and linear or nonlinear complementarity. Their foundations and different applications related with project evaluation are explored. As a matter of fact, these new tools are closely related among them and can treat more complex cases involving -for example- the reaction of agents to policies or the existence of multiple agents in an environment characterized by common functions representing demands or constraints on polluting emissions.

Constraint satisfaction techniques under uncertain conditions for fault diagnosis in nonlinear dynamic systems

The speed of fault isolation is crucial for the design and reconfiguration of fault tolerant control (FTC). In this paper the fault isolation problem is stated as a constraint satisfaction problem (CSP) and solved using constraint propagation techniques. The proposed method is based on constraint satisfaction techniques and uncertainty space refining of interval parameters. In comparison with other approaches based on adaptive observers, the major advantage of the presented method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements and model errors and without the monotonicity assumption. In order to illustrate the proposed approach, a case study of a nonlinear dynamic system is presented

Assembling genes from predicted exons in linear time with dynamic programming

In a number of programs for gene structure prediction in higher eukaryotic genomic sequences, exon prediction is decoupled from gene assembly: a large pool of candidate exons is predicted and scored from features located in the query DNA sequence, and candidate genes are assembled from such a pool as sequences of nonoverlapping frame-compatible exons. Genes are scored as a function of the scores of the assembled exons, and the highest scoring candidate gene is assumed to be the most likely gene encoded by the query DNA sequence. Considering additive gene scoring functions, currently available algorithms to determine such a highest scoring candidate gene run in time proportional to the square of the number of predicted exons. Here, we present an algorithm whose running time grows only linearly with the size of the set of predicted exons. Polynomial algorithms rely on the fact that, while scanning the set of predicted exons, the highest scoring gene ending in a given exon can be obtained by appending the exon to the highest scoring among the highest scoring genes ending at each compatible preceding exon. The algorithm here relies on the simple fact that such highest scoring gene can be stored and updated. This requires scanning the set of predicted exons simultaneously by increasing acceptor and donor position. On the other hand, the algorithm described here does not assume an underlying gene structure model. Indeed, the definition of valid gene structures is externally defined in the so-called Gene Model. The Gene Model specifies simply which gene features are allowed immediately upstream which other gene features in valid gene structures. This allows for great flexibility in formulating the gene identification problem. In particular it allows for multiple-gene two-strand predictions and for considering gene features other than coding exons (such as promoter elements) in valid gene structures.

A randomized concave programming method for choice network revenue management

Models incorporating more realistic models of customer behavior, as customers choosing froman offer set, have recently become popular in assortment optimization and revenue management.The dynamic program for these models is intractable and approximated by a deterministiclinear program called the CDLP which has an exponential number of columns. However, whenthe segment consideration sets overlap, the CDLP is difficult to solve. Column generationhas been proposed but finding an entering column has been shown to be NP-hard. In thispaper we propose a new approach called SDCP to solving CDLP based on segments and theirconsideration sets. SDCP is a relaxation of CDLP and hence forms a looser upper bound onthe dynamic program but coincides with CDLP for the case of non-overlapping segments. Ifthe number of elements in a consideration set for a segment is not very large (SDCP) can beapplied to any discrete-choice model of consumer behavior. We tighten the SDCP bound by(i) simulations, called the randomized concave programming (RCP) method, and (ii) by addingcuts to a recent compact formulation of the problem for a latent multinomial-choice model ofdemand (SBLP+). This latter approach turns out to be very effective, essentially obtainingCDLP value, and excellent revenue performance in simulations, even for overlapping segments.By formulating the problem as a separation problem, we give insight into why CDLP is easyfor the MNL with non-overlapping considerations sets and why generalizations of MNL posedifficulties. We perform numerical simulations to determine the revenue performance of all themethods on reference data sets in the literature.

A new compact linear programming formulation for choice network revenue management

The choice network revenue management model incorporates customer purchase behavioras a function of the offered products, and is the appropriate model for airline and hotel networkrevenue management, dynamic sales of bundles, and dynamic assortment optimization.The optimization problem is a stochastic dynamic program and is intractable. A certainty-equivalencerelaxation of the dynamic program, called the choice deterministic linear program(CDLP) is usually used to generate dyamic controls. Recently, a compact linear programmingformulation of this linear program was given for the multi-segment multinomial-logit (MNL)model of customer choice with non-overlapping consideration sets. Our objective is to obtaina tighter bound than this formulation while retaining the appealing properties of a compactlinear programming representation. To this end, it is natural to consider the affine relaxationof the dynamic program. We first show that the affine relaxation is NP-complete even for asingle-segment MNL model. Nevertheless, by analyzing the affine relaxation we derive a newcompact linear program that approximates the dynamic programming value function betterthan CDLP, provably between the CDLP value and the affine relaxation, and often comingclose to the latter in our numerical experiments. When the segment consideration sets overlap,we show that some strong equalities called product cuts developed for the CDLP remain validfor our new formulation. Finally we perform extensive numerical comparisons on the variousbounds to evaluate their performance.

Recursive contracts

We obtain a recursive formulation for a general class of contractingproblems involving incentive constraints. Under these constraints,the corresponding maximization (sup) problems fails to have arecursive solution. Our approach consists of studying the Lagrangian.We show that, under standard assumptions, the solution to theLagrangian is characterized by a recursive saddle point (infsup)functional equation, analogous to Bellman's equation. Our approachapplies to a large class of contractual problems. As examples, westudy the optimal policy in a model with intertemporal participationconstraints (which arise in models of default) and intertemporalcompetitive constraints (which arise in Ramsey equilibria).

On the throughput-WIP trade-off in queueing systems, diminishing returns and the threshold property: A linear programming approach

We present a new unifying framework for investigating throughput-WIP(Work-in-Process) optimal control problems in queueing systems,based on reformulating them as linear programming (LP) problems withspecial structure: We show that if a throughput-WIP performance pairin a stochastic system satisfies the Threshold Property we introducein this paper, then we can reformulate the problem of optimizing alinear objective of throughput-WIP performance as a (semi-infinite)LP problem over a polygon with special structure (a thresholdpolygon). The strong structural properties of such polygones explainthe optimality of threshold policies for optimizing linearperformance objectives: their vertices correspond to the performancepairs of threshold policies. We analyze in this framework theversatile input-output queueing intensity control model introduced byChen and Yao (1990), obtaining a variety of new results, including (a)an exact reformulation of the control problem as an LP problem over athreshold polygon; (b) an analytical characterization of the Min WIPfunction (giving the minimum WIP level required to attain a targetthroughput level); (c) an LP Value Decomposition Theorem that relatesthe objective value under an arbitrary policy with that of a giventhreshold policy (thus revealing the LP interpretation of Chen andYao's optimality conditions); (d) diminishing returns and invarianceproperties of throughput-WIP performance, which underlie thresholdoptimality; (e) a unified treatment of the time-discounted andtime-average cases.

Implementing TURF analysis through binary linear programming

This paper introduces the approach of using Total Unduplicated Reach and Frequency analysis (TURF) to design a product line through a binary linear programming model. This improves the efficiency of the search for the solution to the problem compared to the algorithms that have been used to date. The results obtained through our exact algorithm are presented, and this method shows to be extremely efficient both in obtaining optimal solutions and in computing time for very large instances of the problem at hand. Furthermore, the proposed technique enables the model to be improved in order to overcome the main drawbacks presented by TURF analysis in practice.

Restless bandits, linear programming relaxations and a primal-dual index heuristic

We develop a mathematical programming approach for the classicalPSPACE - hard restless bandit problem in stochastic optimization.We introduce a hierarchy of n (where n is the number of bandits)increasingly stronger linear programming relaxations, the lastof which is exact and corresponds to the (exponential size)formulation of the problem as a Markov decision chain, while theother relaxations provide bounds and are efficiently computed. Wealso propose a priority-index heuristic scheduling policy fromthe solution to the first-order relaxation, where the indices aredefined in terms of optimal dual variables. In this way wepropose a policy and a suboptimality guarantee. We report resultsof computational experiments that suggest that the proposedheuristic policy is nearly optimal. Moreover, the second-orderrelaxation is found to provide strong bounds on the optimalvalue.

Comparison of human chromosome 21 conserved nongenic sequences (CNGs) with the mouse and dog genomes shows that their selective constraint is independent of their genic environment.

The analysis of conservation between the human and mouse genomes resulted in the identification of a large number of conserved nongenic sequences (CNGs). The functional significance of this nongenic conservation remains unknown, however. The availability of the sequence of a third mammalian genome, the dog, allows for a large-scale analysis of evolutionary attributes of CNGs in mammals. We have aligned 1638 previously identified CNGs and 976 conserved exons (CODs) from human chromosome 21 (Hsa21) with their orthologous sequences in mouse and dog. Attributes of selective constraint, such as sequence conservation, clustering, and direction of substitutions were compared between CNGs and CODs, showing a clear distinction between the two classes. We subsequently performed a chromosome-wide analysis of CNGs by correlating selective constraint metrics with their position on the chromosome and relative to their distance from genes. We found that CNGs appear to be randomly arranged in intergenic regions, with no bias to be closer or farther from genes. Moreover, conservation and clustering of substitutions of CNGs appear to be completely independent of their distance from genes. These results suggest that the majority of CNGs are not typical of previously described regulatory elements in terms of their location. We propose models for a global role of CNGs in genome function and regulation, through long-distance cis or trans chromosomal interactions.

Programming and analysing measurement experiments using the PicoCricket system

The aim of this project is to get used to another kind of programming. Since now, I used very complex programming languages to develop applications or even to program microcontrollers, but PicoCricket system is the evidence that we don’t need so complex development tools to get functional devices. PicoCricket system is the clear example of simple programming to make devices work the way we programmed it. There’s an easy but effective way to program small, devices just saying what we want them to do. We cannot do complex algorithms and mathematical operations but we can program them in a short time. Nowadays, the easier and faster we produce, the more we earn. So the tendency is to develop fast, cheap and easy, and PicoCricket system can do it.

Constraint and cost of oxidative stress on reproduction: correlative evidence in laboratory mice and review of the literature.

ABSTRACT: BACKGROUND: One central concept in evolutionary ecology is that current and residual reproductive values are negatively linked by the so-called cost of reproduction. Previous studies examining the nature of this cost suggested a possible involvement of oxidative stress resulting from the imbalance between pro- and anti-oxidant processes. Still, data remain conflictory probably because, although oxidative damage increases during reproduction, high systemic levels of oxidative stress might also constrain parental investment in reproduction. Here, we investigated variation in oxidative balance (i.e. oxidative damage and antioxidant defences) over the course of reproduction by comparing female laboratory mice rearing or not pups. RESULTS: A significant increase in oxidative damage over time was only observed in females caring for offspring, whereas antioxidant defences increased over time regardless of reproductive status. Interestingly, oxidative damage measured prior to reproduction was negatively associated with litter size at birth (constraint), whereas damage measured after reproduction was positively related to litter size at weaning (cost). CONCLUSIONS: Globally, our correlative results and the review of literature describing the links between reproduction and oxidative stress underline the importance of timing/dynamics when studying and interpreting oxidative balance in relation to reproduction. Our study highlights the duality (constraint and cost) of oxidative stress in life-history trade-offs, thus supporting the theory that oxidative stress plays a key role in life-history evolution.

Report to the Governor and General Assembly on the Status of Out-of School Arts Programming for Youth, December 2006

In the 2006 Iowa General Assembly, House File 2797 called for a study on the status of afterschool arts programs and appropriated $5,000 for the study. In accordance with the legislation, the Iowa Arts Council, who received the charge, contracted with the Iowa Afterschool Alliance to form a Resource Group of out-of-school arts providers and experts to develop and oversee the study, review its results, and make recommendations for the expansion of arts programs that operate outside the normal school day. As a part of its charge in HF 2797, the Iowa Arts Council also documented a sampling of out-of-school arts programs statewide. Five are featured in this report.

Gauge-invariant actions from constraint Hamiltonian dynamics

Dirac's constraint Hamiltonian formalism is used to construct a gauge-invariant action for the massive spin-one and -two fields.

Improving Offender Programming, Data Download, Iowa Department of Corrections, June 2012

In 2008, the Iowa Department of Corrections embarked on a comprehensive audit of all institutional andcommunity corrections offender programs that receive corrections funding. The audit focused on the degree towhich each program adheres to evidence-based practices principles (what works best). The audit areas are listedbelow along with some examples of the questions asked about each program: