DR Philippe Baptiste | |
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Born | March 28, 1972 |
Nationality | French |
Alma mater | University of Technology of Compiègne, University of Strathclyde, Sorbonne University |
Occupation(s) | Academician, Civil Engineer, Scientist |
Title | CEO of CNES |
Philippe Baptiste (born March 28, 1972) is a French engineer, academic and researcher. Baptiste is most well known as the president of the National Centre for Space Studies CNES in addition to his several books and scientific publications and communications in the field of algorithms, combinatorial optimization, operational research and artificial intelligence. [1]
Baptiste was born on March 28, 1972, in France. Baptise holds a PHD in computer science from the University of Technology of Compiègne additionally he is a civil engineering graduate from the Ecole des Mines engineering school in Nancy. Baptise also holds a Msc from the University of Strathclyde, Glasgow and holds a DEA postgraduate diploma from Sorbonne University and is a research director. [1] [2] Baptiste specialises in operational research and artificial intelligence(AI), combinatorial optimisation, and algorithms.
In 1999 during his academic career, Baptiste was a researcher at the French National scientific research centre (CNRS), in addition to IBM's Watson Research Center during 2000 to 2001. [1]
Furthermore, for more than ten years during 2001 to 2012 Baptiste was a lecturer at France's Ecole Polytechnique engineering school. During this time he published several books and around 150 scientific papers, and became the head of Ecole Polytechnique engineering school's information technology laboratory and is credited with creating the Institute of Information Sciences and Interactions prior to being appointed Associate Director General in 2014 of CNRS. [1]
In 2016, Baptiste was appointed as Chief Scientific Officer and later in 2017 named Chief Technology Officer of oil, natural gas, and speciality chemicals company Total. Furthermore, Baptiste has aided in founding and developing a number of start-ups and followed several collaborations with digital, defence and aviation manufactures. [1]
During May 2017 to 2019, Batiste was named chief of staff to French-based biochemist, academic administrator, and politician who served as Minister of Higher Education Frédérique Vidal. After which in 2019 Batiste was an advisor to the French prime Minister Édouard Philippe, During this time Batiste was in close connection with the space policy. [1]
During 2020, Baptiste was appointed Partner and Director of American global management consulting firm Boston Consulting Group. [1]
During 1999, Batisete was awarded the Prix Robert Faure award by the French Operations Research & Decision Support Society(ROADEF), a non-profit society that aims to promote scientific fields of operations Research and Decision in France. The award is in tribute to Professor Robert Faure and available to younger researchers (under 36) who are members of ROADEF, and is awarded every 3 years. [3]
In November 2000, Batiste was awarded the Cor Baayen Award for his PHD thesis in an ERCIM country which include: Cyprus, Poland, France, Germany, Austria, Greece, Italy, Norway, Portugal, The Netherlands, Finland and Sweden. Baptiste won this award fue to the quality of his PHD thesis and his previous publications and achievements up to the year 2000. [4]
Quadratic programming (QP) is the process of solving certain mathematical optimization problems involving quadratic functions. Specifically, one seeks to optimize a multivariate quadratic function subject to linear constraints on the variables. Quadratic programming is a type of nonlinear programming.
In graph theory, the shortest path problem is the problem of finding a path between two vertices in a graph such that the sum of the weights of its constituent edges is minimized.
Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.
The bin packing problem is an optimization problem, in which items of different sizes must be packed into a finite number of bins or containers, each of a fixed given capacity, in a way that minimizes the number of bins used. The problem has many applications, such as filling up containers, loading trucks with weight capacity constraints, creating file backups in media, splitting a network prefix into multiple subnets, and technology mapping in FPGA semiconductor chip design.
Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard.
A fully polynomial-time approximation scheme (FPTAS) is an algorithm for finding approximate solutions to function problems, especially optimization problems. An FPTAS takes as input an instance of the problem and a parameter ε > 0. It returns as output a value which is at least times the correct value, and at most times the correct value.
Guillotine cutting is the process of producing small rectangular items of fixed dimensions from a given large rectangular sheet, using only guillotine-cuts. A guillotine-cut is a straight bisecting line going from one edge of an existing rectangle to the opposite edge, similarly to a paper guillotine.
Uniform machine scheduling is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling. We are given n jobs J1, J2, ..., Jn of varying processing times, which need to be scheduled on m different machines. The goal is to minimize the makespan - the total time required to execute the schedule. The time that machine i needs in order to process job j is denoted by pi,j. In the general case, the times pi,j are unrelated, and any matrix of positive processing times is possible. In the specific variant called uniform machine scheduling, some machines are uniformly faster than others. This means that, for each machine i, there is a speed factor si, and the run-time of job j on machine i is pi,j = pj / si.
Single-machine scheduling or single-resource scheduling is an optimization problem in computer science and operations research. We are given n jobs J1, J2, ..., Jn of varying processing times, which need to be scheduled on a single machine, in a way that optimizes a certain objective, such as the throughput.
Optimal job scheduling is a class of optimization problems related to scheduling. The inputs to such problems are a list of jobs and a list of machines. The required output is a schedule – an assignment of jobs to machines. The schedule should optimize a certain objective function. In the literature, problems of optimal job scheduling are often called machine scheduling, processor scheduling, multiprocessor scheduling, or just scheduling.
Job-shop scheduling, the job-shop problem (JSP) or job-shop scheduling problem (JSSP) is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling. In a general job scheduling problem, we are given n jobs J1, J2, ..., Jn of varying processing times, which need to be scheduled on m machines with varying processing power, while trying to minimize the makespan – the total length of the schedule. In the specific variant known as job-shop scheduling, each job consists of a set of operationsO1, O2, ..., On which need to be processed in a specific order. Each operation has a specific machine that it needs to be processed on and only one operation in a job can be processed at a given time. A common relaxation is the flexible job shop, where each operation can be processed on any machine of a given set.
The Coffman–Graham algorithm is an algorithm for arranging the elements of a partially ordered set into a sequence of levels. The algorithm chooses an arrangement such that an element that comes after another in the order is assigned to a lower level, and such that each level has a number of elements that does not exceed a fixed width bound W. When W = 2, it uses the minimum possible number of distinct levels, and in general it uses at most 2 − 2/W times as many levels as necessary.
Parallel task scheduling is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling. In a general job scheduling problem, we are given n jobs J1, J2, ..., Jn of varying processing times, which need to be scheduled on m machines while trying to minimize the makespan - the total length of the schedule. In the specific variant known as parallel-task scheduling, all machines are identical. Each job j has a length parameter pj and a size parameter qj, and it must run for exactly pj time-steps on exactly qj machines in parallel.
Identical-machines scheduling is an optimization problem in computer science and operations research. We are given n jobs J1, J2, ..., Jn of varying processing times, which need to be scheduled on m identical machines, such that a certain objective function is optimized, for example, the makespan is minimized.
Unrelated-machines scheduling is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling. We need to schedule n jobs J1, J2, ..., Jn on m different machines, such that a certain objective function is optimized. The time that machine i needs in order to process job j is denoted by pi,j. The term unrelated emphasizes that there is no relation between values of pi,j for different i and j. This is in contrast to two special cases of this problem: uniform-machines scheduling - in which pi,j = pi / sj, and identical-machines scheduling - in which pi,j = pi.
Longest-processing-time-first (LPT) is a greedy algorithm for job scheduling. The input to the algorithm is a set of jobs, each of which has a specific processing-time. There is also a number m specifying the number of machines that can process the jobs. The LPT algorithm works as follows:
Balanced number partitioning is a variant of multiway number partitioning in which there are constraints on the number of items allocated to each set. The input to the problem is a set of n items of different sizes, and two integers m, k. The output is a partition of the items into m subsets, such that the number of items in each subset is at most k. Subject to this, it is required that the sums of sizes in the m subsets are as similar as possible.
Matroid-constrained number partitioning is a variant of the multiway number partitioning problem, in which the subsets in the partition should be independent sets of a matroid. The input to this problem is a set S of items, a positive integer m, and some m matroids over the same set S. The goal is to partition S into m subsets, such that each subset i is an independent set in matroid i. Subject to this constraint, some objective function should be minimized, for example, minimizing the largest sum item sizes in a subset. In a more general variant, each of the m matroids has a weight function, which assigns a weight to each element of the ground-set. Various objective functions have been considered. For each of the three operators max,min,sum, one can use this operator on the weights of items in each subset, and on the subsets themselves. All in all, there are 9 possible objective functions, each of which can be maximized or minimized.
The configuration linear program (configuration-LP) is a linear programming technique used for solving combinatorial optimization problems. It was introduced in the context of the cutting stock problem. Later, it has been applied to the bin packing and job scheduling problems. In the configuration-LP, there is a variable for each possible configuration - each possible multiset of items that can fit in a single bin. Usually, the number of configurations is exponential in the problem size, but in some cases it is possible to attain approximate solutions using only a polynomial number of configurations.
Fractional job scheduling is a variant of optimal job scheduling in which it is allowed to break jobs into parts and process each part separately on the same or a different machine. Breaking jobs into parts may allow for improving the overall performance, for example, decreasing the makespan. Moreover, the computational problem of finding an optimal schedule may become easier, as some of the optimization variables become continuous. On the other hand, breaking jobs apart might be costly.