arXiv subjects

Jean-Philippe Chancelier

Publications and source records attributed to Jean-Philippe Chancelier.

At least 55 records · Page 3Linked to original sources

arXiv2021

Constant Along Primal Rays Conjugacies and the l0 Pseudonorm

The so-called l0 pseudonorm on Rd counts the number of nonzero components of a vector. It is used in sparse optimization, either as criterion or in the constraints, to obtain solutions with few nonzero entries. For such problems, the Fenchel conjugacy fails to provide relevant analysis: indeed, the Fenchel conjugate of the characteristic function of the level sets of the l0 pseudonorm is minus infinity, and the Fenchel biconjugate of the l0 pseudonorm is zero. In this paper, we display a class of conjugacies that are suitable for the l0 pseudonorm. For this purpose, we suppose given a (source) norm on Rd. With this norm, we define, on the one hand, a sequence of so-called coordinate-k norms and, on the other hand, a coupling between Rd and Rd , called Capra (constant along primal rays). Then, we provide formulas for the Capra-conjugate and biconjugate, and for the Capra subdifferentials, of functions of the l0 pseudonorm (hence, in particular, of the l0 pseudonorm itself and of the characteristic functions of its level sets), in terms of the coordinate-k norms. As an application, we provide a new family of lower bounds for the l0 pseudonorm, as a fraction between two norms, the denominator being any norm.

math.OC↗

arXiv2021

Best Convex Lower Approximations of the l 0 Pseudonorm on Unit Balls

Whereas the norm of a vector measures amplitude (and is a 1-homogeneous function), sparsity is measured by the 0-homogeneous l0 pseudonorm, which counts the number of nonzero components. We propose a family of conjugacies suitable for the analysis of 0-homogeneous functions. These conjugacies are derived from couplings between vectors, given by their scalar product divided by a 1-homogeneous normalizing factor. With this, we characterize the best convex lower approximation of a 0-homogeneous function on the unit ''ball'' of a normalization function (i.e. a norm without the requirement of subadditivity). We do the same with the best convex and 1-homogeneous lower approximation. In particular, we provide expressions for the tightest convex lower approximation of the l0 pseudonorm on any unit ball, and we show that the tightest norm which minorizes the l0 pseudonorm on the unit ball of any lp-norm is the l1-norm. We also provide the tightest convex lower convex approximation of the l0 pseudonorm on the unit ball of any norm.

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arXiv2021

A Decomposition Method by Interaction Prediction for the Optimization of Maintenance Scheduling

Optimizing maintenance scheduling is a major issue to improve the performance of hydropower plants. We study a system of several physical components of the same family: either a set of turbines, a set of transformers or a set of generators. The components share a common stock of spare parts and experience random failures that occur according to known failure distributions. We seek a deterministic preventive maintenance strategy that minimizes an expected cost depending on maintenance and forced outages of the system. The Auxiliary Problem Principle is used to decompose the original large-scale optimization problem into a sequence of independent subproblems of smaller dimension while ensuring their coordination. Each subproblem consists in optimizing the maintenance on a single component. Decomposition-coordination techniques are based on variational techniques but the maintenance optimization problem is a mixed-integer problem. Therefore, we relax the dynamics and the cost functions of the system. The resulting algorithm iteratively solves the subproblems on the relaxed system with a blackbox method and coordinates the components. Relaxation parameters have an important influence on the optimization and must be appropriately chosen. An admissible maintenance strategy is then derived from the resolution of the relaxed problem. We apply the decomposition algorithm on a system with 80 components. It outperforms the reference blackbox method applied directly on the original problem.

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arXiv2021

Conditional Infimum and Hidden Convexity in Optimization

Detecting hidden convexity is one of the tools to address nonconvex minimization problems. After giving a formal definition of hidden convexity, we introduce the notion of conditional infimum, as it will prove instrumental in detecting hidden convexity. We develop the theory of the conditional infimum, and we establish a tower property, relevant for minimization problems. Thus equipped, we provide a sufficient condition for hidden convexity in nonconvex minimization problems. We illustrate our result on nonconvex quadratic minimization problems. We conclude with perspectives for using the conditional infimum in relation to the so-called S-procedure, to couplings and conjugacies, and to lower bound convex programs.

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arXiv2020

Constant Along Primal Rays Conjugacies and Generalized Convexity for Functions of the Support

The support of a vector in R d is the set of indices with nonzero entries. Functions of the support have the property to be 0-homogeneous and, because of that, the Fenchel conjugacy fails to provide relevant analysis. In this paper, we define the coupling Capra between R d and itself by dividing the classic Fenchel scalar product coupling by a given (source) norm on R d. Our main result is that, when both the source norm and its dual norm are orthant-strictly monotonic, any nondecreasing finite-valued function of the support mapping is Capra-convex, that is, is equal to its Capra-biconjugate (generalized convexity). We also establish that any such function is the composition of a proper convex lower semi continuous function on R d with the normalization mapping on the unit sphere (hidden convexity), and that, when normalized, it admits a variational formulation, which involves a family of generalized local-K-support dual norms.

math.OC↗

arXiv2020

Tropical Dynamic Programming for Lipschitz Multistage Stochastic Programming

We present an algorithm called Tropical Dynamic Programming (TDP) which builds upper and lower approximations of the Bellman value functions in risk-neutral Multistage Stochastic Programming (MSP), with independent noises of finite supports. To tackle the curse of dimensionality, popular parametric variants of Approximate Dynamic Programming approximate the Bellman value function as linear combinations of basis functions. Here, Tropical Dynamic Programming builds upper (resp. lower) approximations of a given value function as min-plus linear (resp. max-plus linear) combinations of "basic functions". At each iteration, TDP adds a new basic function to the current combination following a deterministic criterion introduced by Baucke, Downward and Zackeri in 2018 for a variant of Stochastic Dual Dynamic Programming. We prove, for every Lipschitz MSP, the asymptotic convergence of the generated approximating functions of TDP to the Bellman value functions on sets of interest. We illustrate this result on MSP with linear dynamics and polyhedral costs.

math.OC↗

arXiv2020

A stochastic algorithm for deterministic multistage optimization problems

Several attempts to dampen the curse of dimensionnality problem of the Dynamic Programming approach for solving multistage optimization problems have been investigated. One popular way to address this issue is the Stochastic Dual Dynamic Programming method (SDDP) introduced by Perreira and Pinto in 1991 for Markov Decision Processes. Assuming that the value function is convex (for a minimization problem), one builds a non-decreasing sequence of lower (or outer) convex approximations of the value function. Those convex approximations are constructed as a supremum of affine cuts. On continuous time deterministic optimal control problems, assuming that the value function is semiconvex, Zheng Qu, inspired by the work of McEneaney, introduced in 2013 a stochastic max-plus scheme that builds upper (or inner) non-increasing approximations of the value function. In this note, we build a common framework for both the SDDP and a discrete time version of Zheng Qu's algorithm to solve deterministic multistage optimization problems. Our algorithm generates monotone approximations of the value functions as a pointwise supremum, or infimum, of basic (affine or quadratic for example) functions which are randomly selected. We give sufficient conditions on the way basic functions are selected in order to ensure almost sure convergence of the approximations to the value function on a set of interest.

math.OC↗

arXiv2020

Kuhn's Equivalence Theorem for Games in Intrinsic Form

We state and prove Kuhn's equivalence theorem for a new representation of games, the intrinsic form. First, we introduce games in intrinsic form where information is represented by $σ$-fields over a product set. For this purpose, we adapt to games the intrinsic representation that Witsenhausen introduced in control theory. Those intrinsic games do not require an explicit description of the play temporality, as opposed to extensive form games on trees. Second, we prove, for this new and more general representation of games, that behavioral and mixed strategies are equivalent under perfect recall (Kuhn's theorem). As the intrinsic form replaces the tree structure with a product structure, the handling of information is easier. This makes the intrinsic form a new valuable tool for the analysis of games with information.

math.OC↗

arXiv2019

Upper and Lower Bounds for Large Scale Multistage Stochastic Optimization Problems: Application to Microgrid Management

We consider a microgrid where different prosumers exchange energy altogether by the edges of a given network. Each prosumer is located to a node of the network and encompasses energy consumption, energy production and storage capacities (battery, electrical hot water tank). The problem is coupled both in time and in space, so that a direct resolution of the problem for large microgrids is out of reach (curse of dimensionality). By affecting price or resources to each node in the network and resolving each nodal sub-problem independently by Dynamic Programming, we provide decomposition algorithms that allow to compute a set of decomposed local value functions in a parallel manner. By summing the local value functions together, we are able, on the one hand, to obtain upper and lower bounds for the optimal value of the problem, and, on the other hand, to design global admissible policies for the original system. Numerical experiments are conducted on microgrids of different size, derived from data given by the research and development centre Efficacity, dedicated to urban energy transition. These experiments show that the decomposition algorithms give better results than the standard SDDP method, both in terms of bounds and policy values. Moreover, the decomposition methods are much faster than the SDDP method in terms of computation time, thus allowing to tackle problem instances incorporating more than 60 state variables in a Dynamic Programming framework.

math.OC↗

arXiv2019

Equivalence Between Time Consistency and Nested Formula

You are a financial analyst. At the beginning of every week, you are able to rank every pair of stochastic processes starting from that week up to the horizon. Suppose that two processes are equal at the beginning of the week. Your ranking procedure is time consistent if the ranking does not change between this week and the next one. In this paper, we propose a minimalist definition of Time Consistency (TC) between two (assessment) mappings. With very few assumptions, we are able to prove an equivalence between Time Consistency and a Nested Formula (NF) between the two mappings. Thus, in a sense, two assessments are consistent if and only if one is factored into the other. We review the literature and observe that the various definitions of TC (or of NF) are special cases of ours, as they always include additional assumptions. By stripping off these additional assumptions, we present an overview of the literature where the contribution of each author is enlightened.

q-fin.RM↗

arXiv2019

Lower Bound Convex Programs for Exact Sparse Optimization

In exact sparse optimization problems on Rd (also known as sparsity constrained problems), one looks for solution that have few nonzero components. In this paper, we consider problems where sparsity is exactly measured either by the nonconvex l0 pseudonorm (and not by substitute penalty terms) or by the belonging of the solution to a finite union of subsets. Due to the combinatorial nature of the sparsity constraint, such problems do not generally display convexity properties, even if the criterion to minimize is convex. In the most common approach to tackle them, one replaces the sparsity constraint by a convex penalty term, supposed to induce sparsity. Thus doing, one loses the original exact sparse optimization problem, but gains convexity. However, by doing so, it is not clear that one obtains a lower bound of the original exact sparse optimization problem. In this paper, we propose another approach, where we lose convexity but where we gain at keeping the original exact sparse optimization formulation, by displaying lower bound convex minimization programs. For this purpose , we introduce suitable conjugacies, induced by a novel class of one-sided linear couplings. Thus equipped, we present a systematic way to design norms and lower bound convex minimization programs over their unit ball. The family of norms that we display encompasses most of the sparsity inducing norms used in machine learning. Therefore, our approach provides foundation and interpretation for their use.

math.OC↗

arXiv2019

A Suitable Conjugacy for the l0 Pseudonorm

The so-called l0 pseudonorm on R d counts the number of nonzero components of a vector. It is well-known that the l0 pseudonorm is not convex, as its Fenchel biconjugate is zero. In this paper, we introduce a suitable conjugacy, induced by a novel coupling, Caprac, having the property of being constant along primal rays, like the l0 pseudonorm. The Caprac coupling belongs to the class of one-sided linear couplings, that we introduce. We show that they induce conjugacies that share nice properties with the classic Fenchel conjugacy. For the Caprac conjugacy, induced by the coupling Caprac, we prove that the l0 pseudonorm is equal to its biconjugate: hence, the l0 pseudonorm is Caprac-convex in the sense of generalized convexity. As a corollary, we show that the l0 pseudonorm coincides, on the sphere, with a convex lsc function. We also provide expressions for conjugates in terms of two families of dual norms, the 2-k-symmetric gauge norms and the k-support norms.

math.OC↗

arXiv2018

Fenchel-Moreau Conjugation Inequalities with Three Couplings and Application to Stochastic Bellman Equation

Given two couplings between "primal" and "dual" sets, we prove a general implication that relates an inequality involving "primal" sets to a reverse inequality involving the "dual" sets.% More precisely, let be given two "primal" sets $\PRIMAL$, $\PRIMALBIS$and two "dual" sets $\DUAL$, $\DUALBIS$, together with two {coupling} functions $\PRIMAL \overset{\coupling}{\leftrightarrow} \DUAL $ and $\PRIMALBIS \overset{\couplingbis}{\leftrightarrow} \DUALBIS $. We define a new coupling $\SumCoupling{\coupling}{\couplingbis} $ between the "primal" product set~$\PRIMAL \times \PRIMALBIS$ and the "dual" product set $\DUAL \times \DUALBIS$. Then, we consider any bivariate function $\kernel : \PRIMAL \times \PRIMALBIS \to \barRR $ and univariate functions $\fonctionprimal : \PRIMAL \to \barRR $ and $\fonctionprimalbis : \PRIMALBIS \to \barRR $, all defined on the "primal" sets. We prove that $\fonctionprimal\np{\primal} \geq \inf\_{\primalbis \in \PRIMALBIS} \Bp{\kernel\np{\primal, \primalbis} \UppPlus \fonctionprimalbis\np{\primalbis}} $ $ \Rightarrow \SFM{\fonctionprimal}{\coupling}\np{\dual} \leq \inf\_{\dualbis \in \DUALBIS} \Bp{\SFM{\kernel}{\SumCoupling{\coupling}{\couplingbis}}\np{\dual,\dualbis} \UppPlus \SFM{\fonctionprimalbis}{-\couplingbis}\np{\dualbis}} $, where we stress that the Fenchel-Moreau conjugates $\SFM{\fonctionprimal}{\coupling} $ and $\SFM{\fonctionprimalbis}{-\couplingbis}$ are not necessarily taken with the same coupling. We study the equality case, after having established the classical Fenchel inequality but with a general coupling. % We display several applications. We provide a new formula for the Fenchel-Moreau conjugate of a generalized inf-convolution. We obtain formulas with partial Fenchel-Moreau conjugates. Finally, we consider the Bellman equation in stochastic dynamic programming and we provide a "Bellman-like" equation for the Fenchel conjugates of the value functions.

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arXiv2018

Stochastic optimal control of a domestic microgrid equipped with solar panel and battery

Microgrids are integrated systems that gather and operate energy production units to satisfy consumers demands. This paper details different mathematical methods to design the Energy Management System (EMS) of domestic microgrids. We consider different stocks coupled together - a battery, a domestic hot water tank - and decentralized energy production with solar panel. The main challenge of the EMS is to ensure, at least cost, that supply matches demand for all time, while considering the inherent uncertainties of such systems. We benchmark two optimization algorithms to manage the EMS, and compare them with a heuristic. The Model Predictive Control (MPC) is a well known algorithm which models the future uncertainties with a deterministic forecast. By contrast, Stochastic Dual Dynamic Programming (SDDP) models the future uncertainties as probability distributions to compute optimal policies. We present a fair comparison of these two algorithms to control microgrid. A comprehensive numerical study shows that i) optimization algorithms achieve significant gains compared to the heuristic, ii) SDDP outperforms MPC by a few percents, with a reasonable computational overhead.

math.OC↗

arXiv2017

Stochastic decomposition applied to large-scale hydro valleys management

We are interested in optimally controlling a discrete time dynamical system that can be influenced by exogenous uncertainties. This is generally called a Stochas-tic Optimal Control (SOC) problem and the Dynamic Programming (DP) principle is one of the standard way of solving it. Unfortunately, DP faces the so-called curse of dimensionality: the complexity of solving DP equations grows exponentially with the dimension of the variable that is sufficient to take optimal decisions (the so-called state variable). For a large class of SOC problems, which includes important practical applications in energy management, we propose an original way of obtaining near optimal controls. The algorithm we introduce is based on Lagrangian relaxation, of which the application to decomposition is well-known in the deterministic framework. However, its application to such closed-loop problems is not straightforward and an additional statistical approximation concerning the dual process is needed. The resulting methodology is called Dual Approximate Dynamic Programming (DADP). We briefly present DADP, give interpretations and enlighten the error induced by the approximation. The paper is mainly devoted to applying DADP to the management of large hydro valleys. The modeling of such systems is presented, as well as the practical implementation of the methodology. Numerical results are provided on several valleys, and we compare our approach with the state of the art SDDP method.

math.OC↗

arXiv2016

Auxiliary problem principle and inexact variable metric forward-backward algorithm for minimizing the sum of a differentiable function and a convex function

In view of the minimization of a function which is the sum of a differentiable function $f$ and a convex function $g$ we introduce descent methods which can be viewed as produced by inexact auxiliary problem principleor inexact variable metric forward-backward algorithm. Assuming that the global objective function satisfies the Kurdyka-Lojasiewicz inequalitywe prove the convergence of the proposed algorithm weakening assumptions found in previous works.

math.OC↗

arXiv2015

A novel code generation methodology for block diagram modeler and simulators Scicos and VSS

Block operations during simulation in Scicos and VSS environments can naturally be described as Nsp functions. But the direct use of Nsp functions for simulation leads to poor performance since the Nsp language is interpreted, not compiled. The methodology presented in this paper is used to develop a tool for generating efficient compilable code, such as C and ADA, for Scicos and VSS models from these block Nsp functions. Operator overloading and partial evaluation are the key elements of this novel approach. This methodology may be used in other simulation environments such as Matlab/Simulink.

cs.MS↗

arXiv2013

Extensions and applications of ACF mappings

Using a definition of ASF sequences derived from the definition of asymptotic contractions of the final type of ACF, we give some new fixed points theorem for cyclic mappings and alternating mapping which extend results from T.Suzuki and X.Zhang.

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